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Objective: To compare the effectiveness of the manual and semi-automatic segmentation modules of the third-generation software used in computed tomography (CT) images in calculating frontal sinus (FS) and sphenoid sinus (SS) areas and volumes. Methods: CT images of 200 patients (96 female, 104 male) between the ages of 19 and 73 years (mean 37.38 ± 16.32 years) were randomly selected. Volume and area of FS and SS were segmented manually and semi-automatically using InVesalius 3.1.1 software (CTI, Campinas, São Paulo, Brazil). Results: There was a statistically significant difference was found the main SS area in manual mode and semi-automatic mode as 4.80 ± 1.86 cm2 and 4.84 ± 1.83 cm2, respectively. The main SS volumes were in manual mode and semi-automatic mode as 8.44 ± 3.55 cm3 and 9.62 ± 3.21 cm3, respectively. The main FS volumes were in manual mode and semi-automatic mode as 5.32 ± 2.04 cm3 and 6.65 ± 2.70 cm3, respectively. There was a statistically significant difference was found between manual mode and semi-automatic mode in volume calculation (p

The three-dimensional (3D) voxel labeling of lesions requires significant radiologists’ effort in the development of computer-aided detection software. To reduce the time required for the 3D voxel labeling, we aimed to develop a generalized semiautomatic segmentation method based on deep learning via a data augmentation-based domain generalization framework. In this study, we investigated whether a generalized semiautomatic segmentation model trained using two types of lesion can segment previously unseen types of lesion. We targeted lung nodules in chest CT images, liver lesions in hepatobiliary-phase images of Gd-EOB-DTPA-enhanced MR imaging, and brain metastases in contrast-enhanced MR images. For each lesion, the 32 × 32 × 32 isotropic volume of interest (VOI) around the center of gravity of the lesion was extracted. The VOI was input into a 3D U-Net model to define the label of the lesion. For each type of target lesion, we compared five types of data augmentation and two types of input data. For all considered target lesions, the highest dice coefficients among the training patterns were obtained when using a combination of the existing data augmentation-based domain generalization framework and random monochrome inversion and when using the resized VOI as the input image. The dice coefficients were 0.639 ± 0.124 for the lung nodules, 0.660 ± 0.137 for the liver lesions, and 0.727 ± 0.115 for the brain metastases. Our generalized semiautomatic segmentation model could label unseen three types of lesion with different contrasts from the surroundings. In addition, the resized VOI as the input image enables the adaptation to the various sizes of lesions even when the size distribution differed between the training set and the test set.

Background: Accurate quantification of bone, muscle, and their components is still an unmet need in the musculoskeletal field. Current methods to quantify tissue volumes in 3D images are expensive, labor-intensive, and time-consuming; thus, a reliable, valid, and quick application is highly needed.Methods: Tissue Compass is a standalone software for semiautomatic segmentation and automatic quantification of musculoskeletal organs. To validate the software, cross-sectional micro-CT scans images of rat femur (n=19), and CT images of hip and abdomen (n=100) from the Osteoporotic Fractures in Men (MrOS) Study were used to quantify bone, hematopoietic marrow (HBM), and marrow adipose tissue (MAT) using commercial manual software as a comparator. Also, abdominal CT scans (n=100) were used to quantify psoas muscle volumes and intermuscular adipose tissue (IMAT) using the same software. We calculated Pearson's correlation coefficients, individual intra-class correlation coefficients (ICC), and Bland-Altman limits of agreement together with Bland-Altman plots to show the inter- and intra-observer agreement between Tissue Compass and commercially available software.Results: In the animal study, the agreement between Tissue Compass and commercial software was r>0.93 and ICC>0.93 for rat femur measurements. Bland-Altman limits of agreement was -720.89 (-1.5e+04, 13074.00) for MAT, 4421.11 (-1.8e+04, 27149.73) for HBM and -6073.32 (-2.9e+04, 16388.37) for bone. The inter-observer agreement for QCT human study between two observers was r>0.99 and ICC>0.99. Bland-Altman limits of agreement was 0.01 (-0.07, 0.10) for MAT in hip, 0.02 (-0.08, 0.12) for HBM in hip, 0.05 (-0.15, 0.25) for bone in hip, 0.02 (-0.18, 0.22) for MAT in L1, 0.00 (-0.16, 0.16) for HBM in L1, 0.02 (-0.23, 0.27) for bone in L1. The intra-observer agreement for QCT human study between two applications was r>0.997 and ICC>0.99. Bland-Altman limits of agreement was 0.03 (-0.13, 0.20) for MAT in hip, 0.05 (-0.08, 0.18) for HBM in hip, 0.05 (-0.24, 0.34) for bone in hip, -0.02 (-0.34, 0.31) for MAT in L1, -0.14 (-0.44, 0.17) for HBM in L1, -0.29 (-0.62, 0.05) for bone in L1, 0.03 (-0.08, 0.15) for IMAT in psoas, and 0.02 (-0.35, 0.38) for muscle in psoas. Conclusion: Compared to a conventional application, Tissue Compass demonstrated high accuracy and non-inferiority while also facilitating easier analyses. Tissue Compass could become the tool of choice to diagnose tissue loss/gain syndromes in the future by requiring a small number of CT sections to detect tissue volumes and fat infiltration.

This study was conducted to provide diametric and volumetric data of mandibular condyles from patients with Angle class 2 and 3 malocclusions by semiautomatic segmentation based upon cone-beam computed tomography.Cone-beam computed tomography images of 79 patients were analyzed. By using the open-source software "ITK-SNAP", diametric and volumetric measurements of 158 mandibular condyles were performed. Descriptive statistics were calculated for all normally distributed variables. Correlations between patients with Angle class 2 and 3 were calculated with the independent Student t test. P values P

Purpose This study was conducted to elucidate volumetric data of mandibular condyles of orthognathic patients by analyzing cone beam computed tomography images based upon semiautomatic segmentation. Methods Cone beam computed tomography images of 87 patients with malocclusions were analyzed in this retrospective study. Patients were between 17 and 53 years old and diagnosed with Angle class I, II, or III malocclusion. By using the validated open-source software “ITK-SNAP,” the volumetric measurements of 174 mandibular condyles were performed. Volumetric analysis was performed according to intra-subject side differences by paired Student t test. In accordance to inter-subject side, gender, age and type of malocclusion differences bivariate analysis and ANOVA were applied. Results The mean volume for the right condyle was 1.378 ± 0.447 cm3, with a maximum of 2.379 cm3 and a minimum of 0.121 cm3. The mean volume for the left side was 1.435 ± 0.474 cm3, with a maximum of 3.264 cm3 and a minimum of 0.109 cm3. Bivariate analysis indicated a highly significant inter-subject difference between the volume of the left and right mandibular condyles (p p p Conclusion The fact that shape and volume of mandibular condyles show a high susceptibility to pathological alterations and particularly malocclusions makes a precise knowledge about volumetric changes indispensable. Our results show that significant inter-subject differences in condyle volume could be found with respect to the side and gender. Larger volumes could be assessed for the left condyle and for male patients.

The purpose of this study was to evaluate the use of a semiautomatic algorithm to simultaneously segment multiple high-dose-rate (HDR) gynecologic interstitial brachytherapy (ISBT) needles in three-dimensional (3D) transvaginal ultrasound (TVUS) images, with the aim of providing a clinically useful tool for intraoperative implant assessment.A needle segmentation algorithm previously developed for HDR prostate brachytherapy was adapted and extended to 3D TVUS images from gynecologic ISBT patients with vaginal tumors. Two patients were used for refining/validating the modified algorithm and five patients (8-12 needles/patient) were reserved as an unseen test data set. The images were filtered to enhance needle edges, using intensity peaks to generate feature points, and leveraged the randomized 3D Hough transform to identify candidate needle trajectories. Algorithmic segmentations were compared against manual segmentations and calculated dwell positions were evaluated.All 50 test data set needles were successfully segmented with 96% of algorithmically segmented needles having angular differences3° compared with manually segmented needles and the maximum Euclidean distance was2.1 mm. The median distance between corresponding dwell positions was 0.77 mm with 86% of needles having maximum differences3 mm. The mean segmentation time using the algorithm was30 s/patient.We successfully segmented multiple needles simultaneously in intraoperative 3D TVUS images from gynecologic HDR-ISBT patients with vaginal tumors and demonstrated the robustness of the algorithmic approach to image artifacts. This method provided accurate segmentations within a clinically efficient timeframe, providing the potential to be translated into intraoperative clinical use for implant assessment.

OBJECTIVE. This study assessed a machine learning-based dual-energy CT (DECT) tumor analysis prototype for semiautomatic segmentation and radiomic analysis of benign and malignant liver lesions seen on contrast-enhanced DECT. MATERIALS AND METHODS. This institutional review board-approved study included 103 adult patients (mean age, 65 ± 15 [SD] years; 53 men, 50 women) with benign (60/103) or malignant (43/103) hepatic lesions on contrast-enhanced dual-source DECT. Most malignant lesions were histologically proven; benign lesions were either stable on follow-up CT or had characteristic benign features on MRI. Low- and high-kilovoltage datasets were deidentified, exported offline, and processed with the DECT tumor analysis for semiautomatic segmentation of the volume and rim of each liver lesion. For each segmentation, contrast enhancement and iodine concentrations as well as radiomic features were derived for different DECT image series. Statistical analyses were performed to determine if DECT tumor analysis and radiomics can differentiate benign from malignant liver lesions. RESULTS. Normalized iodine concentration and mean iodine concentration in the benign and malignant lesions were significantly different (p < 0.0001-0.0084; AUC, 0.695-0.856). Iodine quantification and radiomic features from lesion rims (AUC, ≤ 0.877) had higher accuracy for differentiating liver lesions compared with the values from lesion volumes (AUC, ≤ 0.856). There was no difference in the accuracies of DECT iodine quantification (AUC, 0.91) and radiomics (AUC, 0.90) for characterizing liver lesions. CONCLUSION. DECT radiomics were more accurate than iodine quantification for differentiating solid benign and malignant hepatic lesions.

The present work aimed to evaluate the reproducibility of radiomics features derived from manual delineation and semiautomatic segmentation after enhancement using the Contrast Limited Adaptive Histogram Equalization (CLAHE) and Adaptive Histogram Equalization (AHE) techniques on a benign tumor of two-dimensional (2D) mammography images. Thirty mammogram images with known benign tumors were obtained from The Cancer Imaging Archive (TCIA) datasets and were randomly selected as subjects. The samples were enhanced for semiautomatic segmentation sets using the Active Contour Model in MATLAB 2019a before analysis by two independent observers. Meanwhile, the images without any enhancement were segmented manually. The samples were divided into three categories: (1) CLAHE images, (2) AHE images, and (3) manual segmented images. Radiomics features were extracted using algorithms provided by MATLAB 2019a software and were assessed with a reliable intra-class correlation coefficient (ICC) score. Radiomics features for the CLAHE group (ICC = 0.890 ± 0.554, p 0.05). Features in all three categories were more robust for the CLAHE compared to the AHE and manual groups. This study shows the existence in variation for the radiomics features extracted from tumor region that are segmented using various image enhancement techniques. Semiautomatic segmentation with image enhancement using CLAHE algorithm gave the best result and was a better alternative than manual delineation as the first two techniques yielded reproducible descriptors. This method should be applicable for predicting outcomes in patient with breast cancer.

PURPOSE The authors conducted this study to provide morphological and volumetric data of recurrent odontogenic keratocysts of the upper and lower jaw to emphasize risk factors in accordance with their radiological appearance and guide clinical decisions for jeopardized patients. METHODS By applying the open-source software "ITK-Snap" on cone-beam computed tomography images, volumetric measurements of histopathologically diagnosed recurrent odontogenic keratocysts could be performed. For statistical investigations, descriptive statistics and independent Student t test were performed. The intraclass correlation coefficient was used to assess intra- and inter-rater reliabilities. P values P

OBJECTIVES The aims of this study were to (a) present a novel morphological contour interpolation (MCI) algorithm based method to evaluate grafted bone alterations following guided bone regeneration (GBR), (b) compare clinical and radiological outcomes of GBR with two different collagen membranes. MATERIALS AND METHODS The data were retrieved from an ongoing randomized controlled trial. Patients were randomly allocated into two groups: (a) control group (CG): Bio-Gide (b) test group (TG): bovine dermis-derived collagen membrane. Cone beam computed tomography examinations were performed 1 week (T0) and 6 months after surgery (T1). PES/WES at T1, grafted bone volume and density changes from T0 to T1 were recorded. RESULTS Thirty-six patients (16/20 in test/control group, respectively) were enrolled in the present study. Excellent inter-observer reliability (ICC ≥ 0.97) was revealed for repeated measurements using this method. Significant volumetric reduction of grafted bone were found in both groups (test group: from 0.60 to 0.39 cm3 , p

Purpose Cone beam computed tomography (CBCT) is an important imaging technique in maxillofacial evaluations. However, application-specific image analysis methods aimed at extracting quantitative information from these images need to be further developed. The aim of this study was to provide a robust and objective method that could assess radiologic changes around dental implants after sinus elevation surgery with simultaneous implant placement. Materials and methods The study was performed retrospectively on patients fulfilling the inclusion criteria. The included patients had been CBCT scanned preoperatively, at baseline (early after surgery), and 6 months postoperatively. In order to quantify the radiologic changes, an image analysis workflow was developed based on the postoperative baseline and 6-month scans. The workflow included metal artefact reduction, registration, and a standardized protocol for semiautomatic segmentation. Validation of different steps of the method was conducted by comparing scans from all time points. Comparison of constant volumes (eg, screws and bony parts not subjected to change) was used. Additionally, the Dice similarity coefficient (DSC) was used to measure the overlap of the segmentations. Results The study included nine maxillary sinuses from six patients. The bone formation was quantified and visualized in 3D. In the validation, no significant differences were found for the constant volumes at the different scanning time points. The DSC showed accurate results with values > 0.92. Conclusion The method presented in this study provides an objective and robust evaluation of bone formation around dental implants. The same methodologies can be applied in other studies of dental CBCT images, eg, for comparison of grafting materials or surgical strategies.

Objective To evaluate the accuracy of semiautomated segmentation of urinary stone size in computed tomography (CT) compared with manual measurement. Materials and Methods A total of 103 patients (32f, 71m ; mean age 52 years±18 that were diagnosed with urolithiasis and collected stones received standardized ex vivo CT-scans and radiography of the stones. Stone size was segmented semiautomatically using commercial software (syngo.via, Siemens, Germany) and compared with manual caliper measurement on digital radiography. Results Mean size was 4.4 mm in CT and 4.6 mm in radiography. Depending on number of stones analyzed per patient, estimation of stone size showed moderate to excellent correlation for both methods. There was no significant difference in overall size measurement. Conclusion Semiautomatic segmentation of urinary stone size in CT is possible and reduces measurement errors, allowing more precise estimation especially for smaller concrements. Neighboring stones may hamper segmentation of stone size.

Introduction The purpose of this study was to evaluate the variations in the volume of periapical lesions scored using a cone-beam computed tomographic periapical index (CBCTPAI) and to develop a new volume-based periapical index. Methods Cone-beam computed tomographic images were obtained from InteleViewer (Intelerad Medical Systems Incorporated, Montreal, Canada). Teeth with a periapical radiolucency or with a history of endodontic treatment were included in this study. Using 3-dimensional medical imaging processing software (Mimics Research; Materialise NV, Leuven, Belgium), the maximum diameter of 273 periapical lesions and their corresponding CBCTPAI score was determined. The software was then used to determine the volume of the lesions using a semiautomatic segmentation technique. Results There was a substantial variation in the volume for CBCTPAI scores 3, 4, and 5, which was demonstrated by the variance and range, thus making it difficult to use the current CBCTPAI as a method to predict volume and treatment outcomes. A new index, the cone-beam computed tomographic periapical volume index (CBCTPAVI), was developed using partition classification analysis. The results for the new index demonstrated high levels of sensitivity, specificity, precision, and area under the curve, all at 0.90 or more, except 1 sensitivity for CBCTPAVI 1 at 0.875. Overall, the accurate classification rate was 98.169%, and the root mean square error rate was low at 0.07. Conclusions The proposed CBCTPAVI will allow clinicians to classify lesions based on their true 3-dimensional size, accurately assess healing of lesions, and predict treatment outcomes.

Objective: To evaluate the degree of similarity between manual and semiautomatic segmentation of soft-tissue sarcomas on magnetic resonance imaging (MRI). Materials and Methods: This was a retrospective study of 15 MRI examinations of patients with histopathologically confirmed soft-tissue sarcomas acquired before therapeutic intervention. Manual and semiautomatic segmentations were performed by three radiologists, working independently, using the software 3D Slicer. The Dice similarity coefficient (DSC) and the Hausdorff distance were calculated in order to evaluate the similarity between manual and semiautomatic segmentation. To compare the two modalities in terms of the tumor volumes obtained, we also calculated descriptive statistics and intraclass correlation coefficients (ICCs). Results: In the comparison between manual and semiautomatic segmentation, the DSC values ranged from 0.871 to 0.973. The comparison of the volumes segmented by the two modalities resulted in ICCs between 0.9927 and 0.9990. The DSC values ranged from 0.849 to 0.979 for intraobserver variability and from 0.741 to 0.972 for interobserver variability. There was no significant difference between the semiautomatic and manual modalities in terms of the segmentation times (p > 0.05). Conclusion: There appears to be a high degree of similarity between manual and semiautomatic segmentation, with no significant difference between the two modalities in terms of the time required for segmentation. Resumo Objetivo: Verificar a similaridade entre as segmentações manual e semiautomática de sarcomas de tecidos moles na ressonância magnética (RM) e a similaridade interobservador e intraobservador entre as segmentações manuais. Materiais e Métodos: Estudo retrospectivo que incluiu 15 exames de RM de pacientes com diagnóstico de sarcoma de tecidos moles realizados antes de intervenções terapêuticas. As segmentações manual e semiautomática foram realizadas por três radiologistas utilizando o software 3D Slicer. O coeficiente de similaridade Dice (CSD) e a distância de Hausdorff foram utilizados para avaliar a similaridade das segmentações. Análise estatística descritiva e coeficiente de correlação intraclasse (CCI) foram realizados para comparar volumes tumorais. Resultados: A comparação dos métodos manual e semiautomático obteve valores de CSD entre 0,871 e 0,973. A comparação dos volumes segmentados pelos dois métodos de segmentação mostrou CCI entre 0,9927 e 0,9990. As análises intraobservador e interobservador obtiveram valores de CSD, respectivamente, de 0,849 a 0,979 e de 0,741 a 0,972. Não houve diferença significativa entre os tempos de segmentação dos métodos semiautomático e manual (p > 0,05). Conclusão: Houve alta similaridade entre as segmentações de sarcomas de tecidos moles obtidas pelos métodos manual e semiautomático, sem diferença significativa para o tempo despendido para as segmentações.

This investigation was performed to analyze and evaluate anatomical and volumetric data of the sphenoid sinus by semiautomatic segmentation of cone beam computed tomography (CBCT) datasets.This analysis was carried out on cone beam computed tomography images of 100 patients. By using the discretionary software "ITK-Snap", the volumetric investigation of 197 sphenoid sinuses was conducted. The statistical analyses were carried out by using the paired and independent Student t-test. In addition, the Pearsons chi-square test was performed. P values P 0.05 were considered significant.The volumetric mean was 4.438 ± 2.434 cm3 for the right and 4.809 ± 3.000 for the left side. Results did not indicate statistically significant differences according to the side, irrespective of gender (P 0.05). The further investigation of differences between male and female patients did assess significant results (P 0.05), males (10.477 ± 3.851 cm3) present a larger total sinus volume than females (8.219 ± 3.574 cm3). In accordance to patients' age (median 54 years), smaller volumes were related to younger patients and larger volumes to older ones. But, no statistically significant results in accordance to age could be detected (χ2 = 1.258; P 0.05).Endoscopic sinus surgery of the vulnerable and intricate anatomy of the sphenoid sinus requires an explicit knowledge and orientation about the anatomical variations such as septums and volumetric capacity. Concerning this matter, semiautomatic segmentation of CBCT images can aid the surgeon in preoperative planning. Our investigation shows that a small sinus volume, that is, affecting female patients and a complex sinus anatomy by the occurrence of a septum, especially located close to neurovascular structures could essentially aggravate endoscopic sphenoid sinus surgery. In this regard, volumetric analysis of the sphenoid sinus by CBCT images can provide presurgical information and facilitates an individualized treatment.

The aim of this study was to provide volumetric data of the pterygopalatine fossa by semiautomatic segmentation based upon cone beam computed tomography.Cone beam computed tomography (CBCT) images of 100 patients were analyzed. By using the open source software "ITK-Snap," the volumetric measurements of 200 pterygopalatine fossae were performed. For statistical investigations paired t test, and independent Student t test were performed. Also, the Pearsons chi-square test was applied. P values P 0.05 were considered significant.The mean volume was 578.376 mm for the right and 560.979 mm for the left side. The results indicated statistically significant differences according to the right and the left pterygopalatine fossa, regardless of gender (P 0.05). The analysis of differences between males and females did not show any significant results (P 0.05), although males present a slightly larger volume than females. According to the median age (59 years), younger patients presented smaller volumes, whereas older patients presented larger volumes. Nevertheless, no statistically significant differences according to age (χ = 3.520; P 0.05) could be found.Clinical intervention with the application of local anesthetics into the complex and vulnerable anatomy of the pterygopalatine fossa makes a thorough knowledge about the volumetric capacity indispensable. Therefore, the semiautomatic segmentation of CBCT images provides a useful, available and validated tool. Our results show that a final injected anesthetic volume larger than 1 ml exceeds the pterygopalatine fossa capacity considerably and could cause complications. To prevent this, volumetric analysis of this region can provide further information and enables an individualized patients' treatment.

Accurate quantification of cartilage is useful for diagnosis and treatment of osteoarthritis (OA) affected knee joints. Image processing techniques are required for clear visualization and quantification of cartilage degradations in different regions in OA affected knee joints. In this work femur articular cartilage were segmented from MRI of knee joint using two semiautomatic methods namely canny edge detection based method and radial search based method. The thickness and volume of cartilage were measured region wise using segmented images. The cartilages were also segmented using standard method under the supervision of radiologists for comparison and to find the accuracy of quantification results of two semiautomatic methods. The results of measurements using semiautomatic segmentation methods shown good accuracy and the errors are limited to less than 5%.

Stereotactic radiosurgery is a minimally-invasive treatment option for a large number of patients with intracranial tumors. As part of the therapy treatment, accurate delineation of brain tumors is of great importance. However, slice-by-slice manual segmentation on T1c MRI could be time-consuming (especially for multiple metastases) and subjective. In our work, we compared several deep convolutional networks architectures and training procedures and evaluated the best model in a radiation therapy department for three types of brain tumors: meningiomas, schwannomas and multiple brain metastases. The developed semiautomatic segmentation system accelerates the contouring process by 2.2 times on average and increases inter-rater agreement from 92.0% to \(96.5\%\).

In this work, we present a methodology for the semiautomatic segmentation of left ventricle of the hearth of mice in echocardiographic images of the murine model for Chagas disease. The methodology presented is based on the active contour model with shape prior. We will show through experimental results the good performance of the model and discuss pros and cons of the methodology.

Purpose: A hybrid planar-SPECT/CT method for tumor dosimetry in 177Lu-DOTATATE therapy, applicable to datasets consisting of multiple conjugate-view images and one SPECT/CT, is developed and evaluated. Methods: The imaging protocol includes conjugate-view imaging at 1, 24, 96, and 168 h post infusion (p.i.) and a SPECT/CT acquisition 24 h p.i. The dosimetry method uses the planar images to estimate the shape of the time–activity concentration curve, which is then rescaled to absolute units using the SPECT-derived activity concentration. The resulting time-integrated activity concentration coefficient (TIACC) is used to calculate the tumor-absorbed dose. Semiautomatic segmentation techniques are applied for tumor delineation in both planar and SPECT images, where the planar image segmentation is accomplished using an active-rays-based technique. The selection of tumors is done by visual inspection of planar and SPECT images and applying a set of criteria concerning the tumor visibility and possible interference from superimposed activity uptakes in the planar images. Five different strategies for determining values from planar regions of interest (ROIs), based on entire or partial ROIs, and with and without background correction, are evaluated. Evaluation is performed against a SPECT/CT-based method on data from six patients where sequential conjugate-view and SPECT/CT imaging have been performed in parallel and against ground truths in Monte Carlo simulated images. The patient data are also used to evaluate the interoperator variability and to assess the validity of the developed criteria for tumor selection. Results: For patient images, the hybrid method produces TIACCs that are on average 6% below those of the SPECT/CT only method, with standard deviations for the relative TIACC differences of 8%–11%. Simulations show that the hybrid and SPECT-based methods estimate the TIACCs to within approximately 10% for tumors larger than around 10 ml, while for smaller tumors, all methods underestimate the TIACCs due to underestimations of the activity concentrations in the SPECT images. The planar image segmentation has a low operator dependence, with a median Dice similarity coefficient of 0.97 between operators. The adopted criteria for tumor selection manage to discriminate the tumors for which the absorbed-dose deviations between the hybrid and SPECT methods are the highest. Conclusions: The hybrid method is found suitable for studies of tumor-absorbed doses in radionuclide therapy, provided that selection criteria regarding the visibility and overlapping activities in the planar images are applied. (Less)

Donor kidney volume (KV) is an increasingly important parameter evaluated before living kidney donation; however, KV measurements on computed tomographic (CT) scanning requires a manually intensive process of manual or semiautomatic segmentation of kidneys with interobserver variation. Renal artery diameter (RAD) is an easier marker to measure, and this study aims to investigate the relationship between donor RAD and KV. Methods A retrospective review of 77 patients who underwent living donor nephrectomy was conducted. Bilateral KVs were measured based on contrast-enhanced CT scan imaging, and renal artery maximum diameter was measured by direct visualization on the arterial phase of transverse CT sections. Results On regression analysis, there was a significant association between the right and left RADs and their ipsilateral KVs with a regression coefficient of 7.9 (95% CI, 1.3–14.5; P = .02) and 9.8 (95% CI, 3.3–16.3; P = .004), respectively. Mean total RAD correlated with total KV with a regression coefficient of 9.3 (95% CI, 3.8–14.7; P = .001) and weakly correlated with estimated glomerular filtration rate with a Pearson coefficient of .10. Conclusions This study demonstrates that renal artery size is positively associated with KV and may be used as an easily measured surrogate marker for kidney size with its attended implications in living donor transplantation.

The authors' aim was to provide volumetric data of mandibular condyles based upon cone beam computed tomography with the means of semiautomatic segmentation.Retrospective chart review of 350 patients (700 mandibular condyles) with cone beam computerized tomography between February 2007 and December 2016. Univariate analysis was performed to analyze associations between variables. P values

Purpose To demonstrate papillary imaging of eyes with optic disc pits (ODP) or optic disc pit associated maculopathy (ODP-M) with ultrahigh-speed swept-source optical coherence tomography (SS-OCT) at 1.68 million A-scans/s. To generate 3D-renderings of the papillary area with 3D volume-reconstructions of the ODP and highly resolved en face images from a single densely-sampled megahertz-OCT (MHz-OCT) dataset for investigation of ODP-characteristics. Methods A 1.68 MHz-prototype SS-MHz-OCT system at 1050 nm based on a Fourier-domain mode-locked laser was employed to acquire high-definition, 3D datasets with a dense sampling of 1600 × 1600 A-scans over a 45° field of view. Six eyes with ODPs, and two further eyes with glaucomatous alteration or without ocular pathology are presented. 3D-rendering of the deep papillary structures, virtual 3D-reconstructions of the ODPs and depth resolved isotropic en face images were generated using semiautomatic segmentation. Results 3D-rendering and en face imaging of the optic disc, ODPs and ODP associated pathologies showed a broad spectrum regarding ODP characteristics. Between individuals the shape of the ODP and the appending pathologies varied considerably. MHz-OCT en face imaging generates distinct top-view images of ODPs and ODP-M. MHz-OCT generates high resolution images of retinal pathologies associated with ODP-M and allows visualizing ODPs with depths of up to 2.7 mm. Conclusions Different patterns of ODPs can be visualized in patients for the first time using 3D-reconstructions and co-registered high-definition en face images extracted from a single densely sampled 1050 nm megahertz-OCT (MHz-OCT) dataset. As the immediate vicinity to the SAS and the site of intrapapillary proliferation is located at the bottom of the ODP it is crucial to image the complete structure and the whole depth of ODPs. Especially in very deep pits, where non-swept-source OCT fails to reach the bottom, conventional swept-source devices and the MHz-OCT alike are feasible and beneficial methods to examine deep details of optic disc pathologies, while the MHz-OCT bears the advantage of an essentially swifter imaging process. Electronic supplementary material The online version of this article (10.1007/s00417-017-3857-9) contains supplementary material, which is available to authorized users.

Purpose To assess variability in corneal ulcer measurements between ophthalmologists and reduce clinician-dependent variability using semiautomated segmentation of the ulcer from photographs. Methods Three ophthalmologists measured 50 patients' eyes for epithelial defects (EDs) and the stromal infiltrate (SI) size using slit-lamp (SL) calipers. SL photographs were obtained. An algorithm was developed for semiautomatic segmenting of the ED and SI in the photographs. Semiautomatic segmentation was repeated 3 times by different users (2 ophthalmologists and 1 trainee). Clinically significant variability was assessed with intraclass correlation coefficients (ICCs) and the percentage of pairwise measurements differing by ≥0.5 mm. Semiautomatic segmentation measurements were compared with manual delineation of the image by a corneal specialist (gold standard) using Dice similarity coefficients. Results Ophthalmologists' reliability in measurements by SL calipers had an ICC from 0.84 to 0.88 between examiners. Measurements by semiautomatic segmentation had an ICC from 0.96 to 0.98. SL measures of ulcers by clinical versus semiautomatic segmentation measures differed by ≥0.5 mm in 24% to 38% versus 8% to 28% (ED height); 30% to 52% versus 12% to 34% (ED width); 26% to 38% versus 10% to 32% (SI height); and 38% to 58% versus 14% to 34% (SI width), respectively. Average Dice similarity coefficients between manual and repeated semiautomatic segmentation ranged from 0.83 to 0.86 for the ED and 0.78 to 0.83 for the SI. Conclusions Variability exists when measuring corneal ulcers, even among ophthalmologists. Photography and computerized methods for quantifying the ulcer size could reduce variability while remaining accurate and impact quantitative measurement endpoints.

Background and objective Recent evidence points toward an active immunological role of intra-abdominal adipose tissue in Crohn disease (CD). We quantified the abdominal adipose tissue compartments using magnetic resonance imaging (MRI) in 27 pediatric patients with CD compared with 14 controls undergoing MRI examination for other reasons. Methods Total (TAAT), subcutaneous (SCAT) and intra-abdominal (IAAT) adipose tissue areas were measured by semiautomatic segmentation on a transverse slice centered on the umbilicus (mean ± standard deviation in square centimeter) using standard T1-weighted sequences. IAAT/TAAT and IAAT/height ratios were calculated and analyzed for associations with disease duration, phenotype, or therapy. Results Patients with CD (median age 15.0 years, range 7.7-17.9, 18/27 boys, median disease duration 29 months, range 0-136) compared to controls (median age 13.9 years, range 3.3-17.8, 4/14 boys) had higher IAAT area (42.3 ± 21.0 vs 28.7 ± 11.6, P = 0.0494) but similar SCAT and TAAT areas (104.6 ± 72.8 vs 96.5 ± 50.8, P = 0.8170 and 146.9 ± 87.3 vs 125.3 ± 61.5, P = 0.7417, respectively). IAAT/TAAT ratio was higher in patients with CD compared to controls (0.32 ± 0.10 vs 0.24 ± 0.04, P = 0.0081). Patients with disease duration >2 years (n = 14) had higher IAAT/TAAT ratio than those with shorter disease and controls (0.35 ± 0.10 vs 0.28 ± 0.08, P = 0.0288 and 0.24 ± 0.04, P = 0.0009, respectively). In these patients, increased IAAT/height ratio was associated with complicated disease (P = 0.043, r = 0.573). No association was found between IAAT/TAAT ratio and actual disease activity or therapy. Conclusions IAAT is increased in pediatric CD and correlates with disease duration. Assessment of IAAT accumulation may be considered in future MRI scores for inflammation and bowel damage in CD and during follow-up of different therapeutic interventions.

© 2019 American Society for Surgery of the Hand Purpose: Anthropometric assessment of bony structures in the body is important for preoperative computer-aided surgery, implant design, finite element modeling, and biomechanical studies investigating joint structure and function. The use of the contralateral limb in surgery and clinical practice relies on the assumption that the right and left limbs of an individual are symmetric. Therefore, the purpose of this study was to quantify the bilateral symmetry of the bony structures of the distal radius using 3-dimensional (3D) computed tomography. Methods: We collected computed tomography images of 37 paired, fresh-frozen, healthy cadaveric male upper limbs (aged 75.4 ± 8.3 years). Three-dimensional reconstructed models were created using semiautomatic segmentation. Using the 3D models, we measured 3D radial inclination, 3D volar tilt, 3D radial height, medial volar cortical angle, middle volar cortical angle, and lateral volar cortical angle and compared them between sides. Results: There were no statistically significant differences measured between right and left distal radius in 37 paired wrists. Mean radial height was 12.81 mm (SD, 1.74 mm) on the left and 12.88 mm (SD, 1.72 mm) on the right. Mean volar tilt was 10.74° (SD, 3.74°) and 10.77° (SD, 3.19°) and radial inclination was 24.05° (SD, 2.63°) and 24.18° (SD, 3.41°) on the left and right, respectively. Mean volar cortical angle across the radius was 140.9° (SD, 7.9°) on the left and 140.1° (SD, 7.9°) on the right. Conclusions: Direct bilateral comparison of the distal radius and wrist joints is useful to predict normal anatomy of the injured radius, because bilateral similarities exist. Clinical relevance: This article provides a comprehensive list of measurements of the distal radius compared bilaterally using a 3D model. From this study, we found that the contralateral radius can be used as a benchmark with which to compare fracture reduction and to manage malunions during the preoperative planning of corrective osteotomies. It can also be used to define normal anatomy.

OBJECTIVES/HYPOTHESIS High-speed videoendoscopy (HSV) has potential to objectively quantify vibratory vocal fold characteristics during phonation. Glottal Analysis Tools (GAT) version 2018, developed in Erlangen, Germany, is software for determining various glottal area waveform (GAW) quantities. Before having GAT analyze HSV videos, segmenters have to define glottis manually across videos in a semiautomatic segmentation protocol. Such interventions are hypothesized to induce variability of subsequent GAW measure computation across segmenters and may attenuate GAT measures' reliability to a certain point. This study explored intersegmenter variability in GAT's GAW measures based on semiautomatic image processing. STUDY DESIGN Cohort study of rater reliability. METHODS In total, 20 HSV videos from normophonic and dysphonic subjects with various laryngeal disorders were selected for this study and segmented by three trained segmenters. They separately segmented glottis areas in the same frame sets of the videos. Upon analysis of GAW, GAT offers 46 measures related to topologic GAW dynamic characteristics, GAW periodicity and perturbation characteristics, and GAW harmonic components. To address GAT's reliability, intersegmenter-based variability in these measures was examined with intraclass correlation coefficient (ICC). RESULTS In general, ICC behavior of the 46 GAW measures across three raters was highly acceptable. ICC of one parameter was moderate (0.5

Objective This study aimed to evaluate the tumor doubling time of invasive lung adenocarcinoma according to the International Association of the Study for Lung Cancer (IASLC)/American Thoracic Society (ATS)/European Respiratory Society (ERS) histologic classification. Materials and methods Among the 2905 patients with surgically resected lung adenocarcinoma, we retrospectively included 172 patients (mean age, 65.6 ± 9.0 years) who had paired thin-section non-contrast chest computed tomography (CT) scans at least 84 days apart with the same CT parameters, along with 10 patients with squamous cell carcinoma (mean age, 70.9 ± 7.4 years) for comparison. Three-dimensional semiautomatic segmentation of nodules was performed to calculate the volume doubling time (VDT), mass doubling time (MDT), and specific growth rate (SGR) of volume and mass. Multivariate linear regression, one-way analysis of variance, and receiver operating characteristic curve analyses were performed. Results The median VDT and MDT of lung cancers were as follows: acinar, 603.2 and 639.5 days; lepidic, 1140.6 and 970.1 days; solid/micropapillary, 232.7 and 221.8 days; papillary, 599.0 and 624.3 days; invasive mucinous, 440.7 and 438.2 days; and squamous cell carcinoma, 149.1 and 146.1 days, respectively. The adjusted SGR of volume and mass of the solid-/micropapillary-predominant subtypes were significantly shorter than those of the acinar-, lepidic-, and papillary-predominant subtypes. The histologic subtype was independently associated with tumor doubling time. A VDT of 465.2 days and an MDT of 437.5 days yielded areas under the curve of 0.791 and 0.795, respectively, for distinguishing solid-/micropapillary-predominant subtypes from other subtypes of lung adenocarcinoma. Conclusion The tumor doubling time of invasive lung adenocarcinoma differed according to the IASCL/ATS/ERS histologic classification.

Purpose To develop a semiautomatic method for rapid segmentation of myocardial tissue phase mapping (TPM) data. Methods Manual segmentation of the myocardium was performed at end-diastole and end-systole. The points in both user-defined masks were then automatically tracked over the entire cardiac cycle using temporal integration of the velocity field. Paths that failed to visit both masks at the expected times were excluded, after which masks for all time points were generated automatically from the accepted paths. Midventricular and basal phase contrast TPM slices from 12 rats were segmented using the proposed method and fully manual segmentation. The results were compared using Dice's metric and Bland–Altman analysis, and interobserver variability was assessed. Results The semiautomatic method reduced the average user input time from 21 min to 1 min per slice. The Dice metrics between the methods were 0.88 ± 0.03 (midventricular) and 0.83 ± 0.06 (basal), and Bland–Altman limits of agreement of peak systolic and diastolic regional velocities were: midventricular: 0.05 ± 0.65 cm/s, −0.02 ± 0.42 cm/s, and −0.03 ± 0.40 cm/s (radial, tangential, longitudinal); basal: −0.04 ± 0.73 cm/s, 0.03 ± 0.60 cm/s, and −0.04 ± 0.48 cm/s (radial, tangential, longitudinal). Interobserver variability following semiautomatic segmentation was lower than for manual segmentation. Conclusion The proposed method reduced the segmentation time substantially and exhibited well-preserved data quality and excellent interobserver limits of agreement. Magn Reson Med 78:1199–1207, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Introduction Our objectives were to assess reliability, validity, and time efficiency of semiautomatic segmentation using Segura software of the nasal and pharyngeal airways, against manual segmentation with point-based analysis with color mapping. Methods Pharyngeal and nasal airways from 10 cone-beam computed tomography image sets were segmented manually and semiautomatically using Segura (University of Alberta, Edmonton, Alberta, Canada). To test intraexaminer and interexaminer reliabilities, semiautomatic segmentation was repeated 3 times by 1 examiner and then by 3 examiners. In addition to volume and surface area, point-based analysis was completed to assess the reconstructed 3-dimensional models from Segura against manual segmentation. The times of both methods of segmentation were also recorded to assess time efficiency. Results The reliability and validity of Segura were excellent (intraclass correlation coefficient, >0.9 for volume and surface area). Part analysis showed small differences between the Segura and manually segmented 3-dimensional models (greatest difference did not exceed 4.3 mm). Time of segmentation using Segura was significantly shorter than that for manual segmentation, 49 ± 11.0 vs 109 ± 9.4 minutes ( P Conclusions Semiautomatic segmentation of the pharyngeal and nasal airways using Segura was found to be reliable, valid, and time efficient. Part analysis with color mapping was the key to explaining differences in upper airway volume and provides meaningful and clinically relevant analysis of 3-dimensional changes.

The cornea is an essential structure for the proper functioning of human vision. It can suffer injuries like tumors, areas of epithelial removal, infections, and post-surgical injuries that need prompt and effective treatment. The affected region’s area evolution monitoring enables the physician to evaluate the treatment effectiveness. This paper presents a semi-automatic method that can assist the physician in monitoring the evolution of corneal lesions. Our approach uses some specialist-marked regions to train a random forest classifier, and then to classify the other image areas as lesion or non-lesion. We extract color information, and, after classification, we apply an active contour operation to the most significant connected component. Our tests show that by marking 5% of the pixels, our method achieves an accuracy of 99.08% and a Dice of 0.85 on average. According to the literature, the segmentation in more than 90% of the images was considered excellent.

We developed a medical image segmentation and preoperative planning application which implements a semiautomatic and a hybrid semiautomatic liver segmentation algorithm. The aim of this study was to evaluate the feasibility of computer-assisted liver tumor surgery using these algorithms which are based on thresholding by pixel intensity value from initial seed points. A random sample of 12 patients undergoing elective high-risk hepatectomies at our institution was prospectively selected to undergo computer-assisted surgery using our algorithms (June 2013-July 2014). Quantitative and qualitative evaluation was performed. The average computer analysis time (segmentation, resection planning, volumetry, visualization) was 45 min/dataset. The runtime for the semiautomatic algorithm was

This thesis has been embargoed for 6 months and will not be available until November 2, 2014.

[EN] This work presents a Positron Emission Tomography (PET) image analysis tool and its application to the study of rat brain glucose consumption (PETra comes from PET+rat). The described methodology has four steps: a preprocessing of PET images, a coregistration of these images with an atlas, a semiautomatic segmentation of the regions of interest in the rat brain and a 3D reconstruction of these regions to obtain the volumes of interest. Brain glucose uptake was quantified as Standardized Uptake Value (SUV). This tool was applied to nine Wistar rats, young (4-7 months) and old (22-24 months) groups, to study the effect of aging on brain glucose consumption and the difference between sexes. Results showed a lower glucose uptake in old rats than in young rats, regardless gender; while young female rats showed higher glucose consumption than young male rats, whereas these differences disappeared with aging. The developed tool allows the quantification of glucose in rat brain. Results show the accuracy of the tool to define ranges of variation in a population of young and old rats, showing a decrease in glucose consumption in aging.

Rationale and Objectives The aim of the present study was to characterize the kinetics of two nanoparticle-based contrast agents for preclinical imaging, Exitron nano 6000 and Exitron nano 12000, and the iodinated agent eXIA 160 in both healthy mice and in a mouse model of hepatocellular carcinoma (HCC). Semiautomatic segmentation of liver lesions for estimation of total tumor load of the liver was evaluated in HCC mice. Materials and Methods The normal time course of contrast enhancement was assessed in 15 healthy C57BL/6 mice. Imaging of tumor spread in the liver was evaluated in 15 mice harboring a transgenic HCC model (ASV-B mice). Automatic segmentation of liver lesions for determination of total tumor burden of the liver was tested in three additional ASV-B mice before and after an experimental therapy. Results In healthy mice, clearance of the contrast agent from blood was completed within 3–4 hours for eXIA 160 and Exitron nano 6000, whereas complete blood clearance of Exitron nano 12000 required about 24 hours. eXIA 160 provided maximum liver contrast at 1 hour post injection (p.i.) followed by a continuous decline. Enhancement of liver contrast with Exitron nano 6000 and Exitron nano 12000 reached a plateau at about 4 hours p.i., which lasted until the end of the measurements at 96 hours p.i. Maximum contrast enhancement of the liver was not statistically different between Exitron nano 6000 and Exitron nano 12000, but was about three times lower for eXIA 160 (P Conclusions Both nanoparticle-based contrast agents provided stronger and longer lasting contrast enhancement of healthy liver parenchyma. Exitron nano 12000 allowed automatic segmentation of tumor lesions for estimation of the total tumor load in the liver.

Quantitative analysis of computed tomography (CT) is essential to the study of acute lung injury. However, quantitative CT is made difficult by poor lung aeration, which complicates the critical step of image segmentation. To overcome this obstacle, this study sought to develop and validate a semiautomated, multilandmark, registration-based scheme for lung segmentation that is effective in conditions of poor aeration. Expiratory and inspiratory CT images were obtained in rats ( n = 8) with surfactant depletion of incremental severity to mimic worsening aeration. Trained operators manually delineated the images to provide a comparative landmark. Semiautomatic segmentation originated from a single, previously segmented reference image obtained at healthy baseline. Deformable registration of the target images (after surfactant depletion) was performed using the symmetric diffeomorphic transformation model with B-spline regularization. Registration used multiple landmarks (i.e., rib cage, spine, and lung parenchyma) to minimize the effect of poor aeration. Then target images were automatically segmented by applying the calculated transformation function to the reference image contour. Semiautomatically and manually segmented contours proved to be highly similar in all aeration conditions, including those characterized by more severe surfactant depletion and expiration. The Dice similarity coefficient was over 0.9 in most conditions, confirming high agreement, irrespective of poor aeration. Furthermore, CT density-based measurements of gas volume, tissue mass, and lung aeration distribution were minimally affected by the method of segmentation. Moving forward, multilandmark registration has the potential to streamline quantitative CT analysis by enabling semiautomatic image segmentation of lungs with a broad range of injury severity.

Manual segmentation of hepatic metastases in ultrasound images acquired from patients suffering from pancreatic cancer is common practice. Semiautomatic measurements promising assistance in this process are often assessed using a small number of lesions performed by examiners who already know the algorithm. In this work, we present the application of an algorithm for the segmentation of liver metastases due to pancreatic cancer using a set of 105 different images of metastases. The algorithm and the two examiners had never assessed the images before. The examiners first performed a manual segmentation and, after five weeks, a semiautomatic segmentation using the algorithm. They were satisfied in up to 90% of the cases with the semiautomatic segmentation results. Using the algorithm was significantly faster and resulted in a median Dice similarity score of over 80%. Estimation of the inter-operator variability by using the intra class correlation coefficient was good with 0.8. In conclusion, the algorithm facilitates fast and accurate segmentation of liver metastases, comparable to the current gold standard of manual segmentation., Comment: 7 pages, 3 Figures, 3 Tables, 46 References

The goal of this study was to develop a semiautomatic segmentation technique of the abdominal aorta to determine the arterial input function (AIF). A total of 24 patients having therapy naive abdominal cancers were imaged using DCE-MRI on a 3T MR scanner. DCE-MRI continued for 4.2 minutes with 2.1 seconds temporal resolution (120 acquisitions). Gadoteridol (0.1 mmol/kg) was infused intravenously at 30 seconds after starting DCE-MRI, and flushed with 20-ml saline (2 ml/s). Patients were instructed to hold breath after maximal inhalation, and repeat as needed to full inspiration. The location of the abdominal aorta was manually identified, but its segmentation and motion tracking were automatically implemented. AIFs determined in the aortic region with and without tracking motion were statistically compared. The aortic region was further segmented into multiple smaller regions, and the AIF change according to the size of the region of interest (ROI) was examined. The displacement of the abdominal aorta during DCE-MRI was 3.4±2.3 (mean±SD) mm. The root mean square error (RMSE) of AIF from the best fitting curve was 0.110±0.010 mM after motion correction, which was significantly smaller than that before motion correction (0.134±0.016 mM; p

Objective: to provide a proof-of-concept tool for segmenting chronic wounds and transmitting the results as instructions and coordinates to a bioprinter robot and thus facilitate the treatment of chronic wounds. Methods: several segmentation methods used for measuring wound geometry, including edge-detection and morphological operations, region-growing, Livewire, active contours, and texture segmentation, were compared on 26 images from 15 subjects. Ground-truth wound delineations were generated by a dermatologist. The wound coordinates were converted into G-code understandable by the bioprinting robot. Due to its desirable properties, alginate hydrogel was synthesized by dissolving 16% (w/v) sodium-alginate and 4% (w/v) gelatin in deionized water and used for cell encapsulation. Results: Livewire achieved the best performance, with minimal user interaction: 97.08%, 99.68% 96.67%, 96.22, 98.15, and 32.26, mean values, respectively, for accuracy, sensitivity, specificity, Jaccard index, Dice similarity coefficient, and Hausdorff distance. The bioprinter robot was able to print skin cells on the surface of skin with a 95.56% similarity between the bioprinted patch's dimensions and the desired wound geometry. Conclusion: we have designed a novel approach for the healing of chronic wounds, based on semiautomatic segmentation of wound images, improving clinicians’ control of the bioprinting process through more accurate coordinates. Significance: this study is the first to perform wound bioprinting based on image segmentation. It also compares several segmentation methods used for this purpose to determine the best.

Electron tomography is a powerful tool for visualizing subcellular organelles. With the advances in cryo-fixation techniques, it is now possible to reconstruct complex structures in cells preserved close to their native states in three-dimension (3D) using electron tomography. In order to better visualize these objects, 3D models are made from outlines of organelles in individual tomographic slices, which can be used to display morphological features and quantify structural parameters. While outlines of simple organelles can be drawn by hand fairly quickly, it is possible to accelerate 3D modeling of more complex organelles by means of semiautomatic segmentation. In this chapter, we use the example of reconstructing Golgi cisternae of a plant cell into 3D models using the semiautomatic protocol.

Rationale and Objectives Hyperpolarized (HP) gas magnetic resonance imaging (MRI) is an advanced imaging technique that provides high-resolution regional information on lung function without using ionizing radiation. Before this modality can be considered for assessing clinical or investigational interventions, baseline repeatability needs to be established. We assessed repeatability of lung function measurement using HP helium-3 MRI (HP 3 He MRI) in a small cohort of patients with cystic fibrosis (CF). Materials and Methods We examined repeatability of HP 3 He MR images of five patients with CF in four scanning sessions over a 4-week period. We acquired images on a Philips 3.0 Tesla Achieva MRI scanner using a quadrature, flexible, wrap-around, 3 He radiofrequency coil with a fast gradient-echo pulse sequence. We determined ventilation volume and ventilation defect volume using an advanced semiautomatic segmentation algorithm and also quantified ventilation heterogeneity. Results There were no significant differences in total ventilation volume, ventilation defect volume, ventilation defect percentage, or mean ventilation heterogeneity (repeated-measures analysis of variance, P = .2116, P = .2825, P = .2871, and P = .7265, respectively) in the patients across the four scanning sessions. Conclusions Our results indicate that total ventilation volume, ventilation defect volume, ventilation defect percentage, and mean ventilation heterogeneity as assessed by HP gas MRI in CF patients with stable health are reproducible over time. This repeatability and the technique's capability to provide noninvasive high-resolution data on regional lung function without ionizing radiation make 3 He MRI a potentially useful outcome measure for CF-related clinical trials.

Contains fulltext : 154962.pdf (Publisher’s version ) (Open Access) To determine whether semiautomatic volumetric software can differentiate part-solid from nonsolid pulmonary nodules and aid quantification of the solid component.As per reference standard, 115 nodules were differentiated into nonsolid and part-solid by two radiologists; disagreements were adjudicated by a third radiologist. The diameters of solid components were measured manually. Semiautomatic volumetric measurements were used to identify and quantify a possible solid component, using different Hounsfield unit (HU) thresholds. The measurements were compared with the reference standard and manual measurements.The reference standard detected a solid component in 86 nodules. Diagnosis of a solid component by semiautomatic software depended on the threshold chosen. A threshold of -300 HU resulted in the detection of a solid component in 75 nodules with good sensitivity (90 \%) and specificity (88 \%). At a threshold of -130 HU, semiautomatic measurements of the diameter of the solid component (mean 2.4 mm, SD 2.7 mm) were comparable to manual measurements at the mediastinal window setting (mean 2.3 mm, SD 2.5 mm [p = 0.63]).Semiautomatic segmentation of subsolid nodules could diagnose part-solid nodules and quantify the solid component similar to human observers. Performance depends on the attenuation segmentation thresholds. This method may prove useful in managing subsolid nodules.• Semiautomatic segmentation can accurately differentiate nonsolid from part-solid pulmonary nodules • Semiautomatic segmentation can quantify the solid component similar to manual measurements • Semiautomatic segmentation may aid management of subsolid nodules following Fleischner Society recommendations • Performance for the segmentation of subsolid nodules depends on the chosen attenuation thresholds.

Determination of acetabular cartilage loss in the hip joint is a clinically significant metric that requires image segmentation. A new semiautomatic method to segment acetabular cartilage in computed tomography (CT) arthrography scans was developed and tested. A semiautomatic segmentation method was developed based on the combination of anatomical and statistical information. Anatomical information is identified using the pelvic bone position and the contact area between cartilage and bone. Statistical information is acquired from CT intensity modeling of acetabular cartilage and adjacent tissue structures. This method was applied to the identification of acetabular cartilages in 37 intra-articular CT arthrography scans. The semiautomatic anatomical–statistical method performed better than other segmentation methods. The semiautomatic method was effective in noisy scans and was able to detect damaged cartilage. The new semiautomatic method segments acetabular cartilage by fully utilizing the statistical and anatomical information in CT arthrography datasets. This method for hip joint cartilage segmentation has potential for use in many clinical applications.

Nowadays, medical diagnostics using images have considerable importance in many areas of medicine. Specifically, diagnoses of cardiac arteries can be performed by means of digital images. Usually, this diagnostic is aided by computational tools. Generally, automated tools designed to aid in coronary heart diseases diagnosis require the coronary artery tree segmentation. This work presents a method for a semiautomatic segmentation of the coronary artery tree in 2D angiograms. In other to achieve that, a hybrid algorithm based on region growing and differential geometry is proposed. For the validation of our proposal, some objective and quantitative metrics are defined allowing us to compare our method with another one proposed in the literature. From the experiments, we observe that, in average, the proposed method here identifies about 90% of the coronary artery tree while the method proposed by Schrijver & Slump (2002) identifies about 80%.

In 2005, an application for surgical planning called AYRA $${\textregistered }$$ was designed and validated by different surgeons and engineers at the Virgen del Rocio University Hospital, Seville (Spain). However, the segmentation methods included in AYRA and in other surgical planning applications are not able to segment accurately tumors that appear in soft tissue. The aims of this paper are to offer an exhaustive validation of an accurate semiautomatic segmentation tool to delimitate retroperitoneal tumors from CT images and to aid physicians in planning both radiotherapy doses and surgery. A panel of 6 experts manually segmented 11 cases of tumors, and the segmentation results were compared exhaustively with: the results provided by a surgical planning tool (AYRA), the segmentations obtained using a radiotherapy treatment planning system (Pinnacle $$^{\textregistered }$$ ), the segmentation results obtained by a group of experts in the delimitation of retroperitoneal tumors and the segmentation results using the algorithm under validation. 11 cases of retroperitoneal tumors were tested. The proposed algorithm provided accurate results regarding the segmentation of the tumor. Moreover, the algorithm requires minimal computational time—an average of 90.5% less than that required when manually contouring the same tumor. A method developed for the semiautomatic selection of retroperitoneal tumor has been validated in depth. AYRA, as well as other surgical and radiotherapy planning tools, could be greatly improved by including this algorithm.

PURPOSE To develop image analysis software usable by nonexpert graders to segment geographic atrophy (GA) from dry AMD and to quantify rim area focal hyperautofluorescence (RAFH) surrounding GA on fundus autofluorescence (FAF) images. To compare the GA progression predictions based on RAFH with those of a validated qualitative classification system. METHODS Retrospective analysis of serial FAF images from 49 eyes of 30 subjects with GA was performed using MATLAB-based software (MathWorks, Natick, MA, USA). Correlation between RAFH and progression of GA was analyzed using Spearman correlation. Comparisons of lesion growth rate between RAFH tertiles used generalized estimating equations and Kruskal-Wallis testing. Interobserver variability in lesion size, growth rate and RAFH were compared between two expert and one nonexpert grader using Bland-Altman statistics. RESULTS Rim area focal hyperautofluorescence was positively correlated with GA progression rate (ρ = 0.49, P < 0.001). Subjects in the middle or highest RAFH tertile were at greater risk of progression (P = 0.005 and P = 0.001, respectively). Mean difference in RAFH was 0.012 between expert and -0.005 to 0.017 between expert and nonexperts. Mean difference in lesion size (mm2) was 0.11 between expert and -0.29 to 0.41 between expert and nonexperts. Mean difference in lesion growth rate (mm2/mo) was 0.0098 between expert and -0.027 to 0.037 between expert and nonexperts. Risk stratification based on RAFH tertile was 96% identical across all graders. CONCLUSIONS Our semiautomated image analysis software facilitates stratification of progression risk based on RAFH and enabled a nonexpert grader with minimal training to obtain results comparable to expert graders. Predictions based on RAFH were similar to those of a validated qualitative classification system.

The aim of this study is to determine the magnitude of change in spleen volume on CT in subjects sustaining blunt abdominal trauma without hemorrhage relative to patients without disease and how the spleen volumes are distributed. Sixty-seven subjects with blunt abdominal trauma and 101 control subjects were included in this retrospective single-center, IRB-approved, and HIPAA-compliant study. Patients with an injured spleen were excluded. Using a semiautomatic segmentation program, two readers computed spleen volumes from CT. Spleen volume distribution in male and female trauma and control cohorts were compared nonparametrically. Spleen volume plotted against height, weight, and age were analyzed by linear regression. The number of females and males are, respectively, 35 and 32 in trauma subjects and 69 and 32 among controls. Female trauma patients (49.6 years) were older than males (39.8 years) (p = 0.02). Distributions of spleen volume were not normal, skewed above their means, requiring a nonparametric comparison. Spleen volumes in trauma patients were smaller than those in controls with medians of 230 vs 294 mL in males(p

Objectives. The aim of this study was to compare a semiautomatic segmentation method to quantify the function of both ventricles in magnetic resonance imaging (MRI) with the manual tracing method. Material and methods. We examined 17 patients with diverse cardiovascular diseases on a 1.5 Tesla MRI unit (Magnetom Symphony Quantum; Siemens Medical Systems, Erlangen, Germany) using the following parameters: maximum gradient, 30 mT/m; and slew rate, 125 T/m/s. In all studies, we acquired images in cine mode in the short axis (SSFP, 6mm slice thickness, from the base to the ventricular apex) with breath holding. To reduce the user interaction, we used only one image per patient to initiate the semiautomatic method. The semiautomatic method was based on a specifically designed algorithm of regional growth and border detection. We quantified the end-diastolic volume (EDV), end-systolic volume (ESV), and the ejection fraction (EF) for both ventricles in all patients. Results. No significant differences between the two segmentation techniques were found in the quantification of either ventricle (p>0.05). The difference in the volumes, although nearly significant, are clinically irrelevant. The correlation for the estimation of left ventricular function was excellent (r>0.9), and the correlation for the estimation of right ventricular function was good (r>0.7). Conclusions. Our semiautomatic segmentation method enables the function of both ventricles to be quantified as accurately as the conventional method.

Remote sensing images are useful for monitoring the spatial distribution and growth of urban built-up areas because they can provide timely and synoptic views of urban land cover. Although the normalized difference built-up index (NDBI) is useful to map urban built-up areas, it still has some limitations. This study sought to improve the NDBI by using a semiautomatic segmentation approach. The proposed approach had more than 20% higher overall accuracy than the original method when both were implemented simultaneously at the National Olympic Park (NOP), Beijing, China. One reason for the improvement is that the proposed NDBI approach separates urban areas from barren and bare land to some extent. More importantly, the proposed method eliminates the original assumption that a positive NDBI value should indicate built-up areas and a positive normalized difference vegetation index (NDVI) value should indicate vegetation. The new method has improved universality and lower commission error compared with the or...