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Scanning everyday objects with depth sensors is the state-of-the-art approach to generating point clouds for realistic 3D representations. However, the resulting point cloud data suffers from outliers and contains irrelevant data from neighboring objects. To obtain only the desired 3D representation, additional manual segmentation steps are required. In this paper, we compare three different technology classes as independent variables (desktop vs. tablet vs. virtual reality) in a within-subject user study (N = 18) to understand their effectiveness and efficiency for such segmentation tasks. We found that desktop and tablet still outperform virtual reality regarding task completion times, while we could not find a significant difference between them in the effectiveness of the segmentation. In the post hoc interviews, participants preferred the desktop due to its familiarity and temporal efficiency and virtual reality due to its given three-dimensional representation. [ABSTRACT FROM AUTHOR]

Abstract Background Myopia affects 1.4 billion individuals worldwide. Notably, there is increasing evidence that choroidal thickness plays an important role in myopia and risk of developing myopia-related conditions. With the advancements in artificial intelligence (AI), choroidal thickness segmentation can now be automated, offering inherent advantages such as better repeatability, reduced grader variability, and less reliance for manpower. Hence, we aimed to evaluate the agreement between AI-automated and manual segmented measurements of subfoveal choroidal thickness (SFCT) using two swept-source optical coherence tomography (OCT) systems. Methods Subjects aged ≥ 16 years, with myopia of ≥ 0.50 diopters in both eyes, were recruited from the Prospective Myopia Cohort Study in Singapore (PROMYSE). OCT scans were acquired using Triton DRI-OCT and PLEX Elite 9000. OCT images were segmented both automatically with an established SA-Net architecture and manually using a standard technique with adjudication by two independent graders. SFCT was subsequently determined based on the segmentation. The Bland–Altman plot and intraclass correlation coefficient (ICC) were used to evaluate the agreement. Results A total of 229 subjects (456 eyes) with mean [± standard deviation (SD)] age of 34.1 (10.4) years were included. The overall SFCT (mean ± SD) based on manual segmentation was 216.9 ± 82.7 µm with Triton DRI-OCT and 239.3 ± 84.3 µm with PLEX Elite 9000. ICC values demonstrated excellent agreement between AI-automated and manual segmented SFCT measurements (PLEX Elite 9000: ICC = 0.937, 95% CI: 0.922 to 0.949, P

The paraventricular nucleus of the hypothalamus (PVN) is uniquely capable of proximal control over autonomic and neuroendocrine stress responses, and the bed nucleus of the stria terminalis (BNST) directly modulates PVN function, as well as playing an important role in stress control itself. The dorsal BNST (dBNST) is predominantly preautonomic, while the ventral BNST (vBNST) is predominantly viscerosensory, receiving dense noradrenergic signaling. Distinguishing the dBNST and vBNST, along with the PVN, may facilitate our understanding of dynamic interactions among these regions. T1-weighted MPRAGE and high resolution gradient echo (GRE) modalities were acquired at 7T. GRE was coregistered to MPRAGE and segmentations were performed in MRIcroGL based on their Atlas of the Human Brain depictions. The dBNST, vBNST and PVN were manually segmented in 25 participants; 10 images were rated by 2 raters. These segmentations were normalized and probabilistic atlases for each region were generated in MNI space, now available as resources for future research. We found moderate–high inter-rater reliability [n = 10; Mean Dice (SD); PVN = 0.69 (0.04); dBNST = 0.77 (0.04); vBNST = 0.62 (0.04)]. Probabilistic atlases were reverse normalized into native space for six additional participants that were segmented but not included in the original 25. We also found moderate to moderate–high reliability between the probabilistic atlases and manual segmentations [n = 6; Mean Dice (SD); PVN = 0.55 (0.12); dBNST = 0.60 (0.10); vBNST = 0.47 (0.12 SD)]. By isolating these hypothalamic and BNST subregions using ultra-high field MRI modalities, more specific delineations of these regions can facilitate greater understanding of mechanisms underlying stress-related function and psychopathology. [ABSTRACT FROM AUTHOR]

Purpose: Delay in diagnosis can contribute to poor outcomes in pancreatic ductal adenocarcinoma (PDAC), and new tools for early detection are required. Recent application of artificial intelligence to cancer imaging has demonstrated great potential in detecting subtle early lesions. The aim of the study was to evaluate global and local accuracies of deep neural network (DNN) segmentation of normal and abnormal pancreas with pancreatic mass. Methods: Our previously developed and reported residual deep supervision network for segmentation of PDAC was applied to segment pancreas using CT images of potential renal donors (normal pancreas) and patients with suspected PDAC (abnormal pancreas). Accuracy of DNN pancreas segmentation was assessed using DICE simulation coefficient (DSC), average symmetric surface distance (ASSD), and Hausdorff distance 95% percentile (HD95) as compared to manual segmentation. Furthermore, two radiologists semi-quantitatively assessed local accuracies and estimated volume of correctly segmented pancreas. Results: Forty-two normal and 49 abnormal CTs were assessed. Average DSC was 87.4 ± 3.1% and 85.5 ± 3.2%, ASSD 0.97 ± 0.30 and 1.34 ± 0.65, HD95 4.28 ± 2.36 and 6.31 ± 6.31 for normal and abnormal pancreas, respectively. Semi-quantitatively, ≥95% of pancreas volume was correctly segmented in 95.2% and 53.1% of normal and abnormal pancreas by both radiologists, and 97.6% and 75.5% by at least one radiologist. Most common segmentation errors were made on pancreatic and duodenal borders in both groups, and related to pancreatic tumor including duct dilatation, atrophy, tumor infiltration and collateral vessels. Conclusion: Pancreas DNN segmentation is accurate in a majority of cases, however, minor manual editing may be necessary; particularly in abnormal pancreas. [ABSTRACT FROM AUTHOR]

Abstract Background There is an increasing interest in assessing paraspinal morphology and composition in relation to low back pain (LBP). However, variations in methods and segmentation protocols contribute to the inconsistent findings in the literature. We present an on-line resource, the ParaspInaL muscLe segmentAtion pRoject (PILLAR, https://projectpillar.github.io/ ), to provide a detailed description and visual guide of a segmentation protocol by using the publicly available ITK-SNAP software and discuss related challenges when performing paraspinal lumbar muscles segmentations from magnetic resonance imaging (MRI). Methods T2-weighted and corresponding fat-water IDEAL axial MRI from 3 males and 3 females (2 chronic LBP and 1 control for each sex) were used to demonstrate our segmentation protocol for each lumbar paraspinal muscle (erector spinae, lumbar multifidus, quadratus lumborum and psoas) and lumbar spinal level (L1-L5). Results Proper segmentation requires an understanding of the anatomy of paraspinal lumbar muscles and the variations in paraspinal muscle morphology and composition due to age, sex, and the presence of LBP or related spinal pathologies. Other challenges in segmentation includes the presence and variations of intramuscular and epimuscular fat, and side-to-side asymmetry. Conclusion The growing interest to assess the lumbar musculature and its role in the development and recurrence of LBP prompted the need for comprehensive and easy-to-follow resources, such as the PILLAR project to reduce inconsistencies in segmentation protocols. Standardizing manual muscle measurements from MRI will facilitate comparisons between studies while the field is progressively moving towards the automatization of paraspinal muscle measurements for large cohort studies.

Background: There is an increasing interest in assessing paraspinal morphology and composition in relation to low back pain (LBP). However, variations in methods and segmentation protocols contribute to the inconsistent findings in the literature. We present an on-line resource, the ParaspInaL muscLe segmentAtion pRoject (PILLAR, https://projectpillar.github.io/), to provide a detailed description and visual guide of a segmentation protocol by using the publicly available ITK-SNAP software and discuss related challenges when performing paraspinal lumbar muscles segmentations from magnetic resonance imaging (MRI). Methods: T2-weighted and corresponding fat-water IDEAL axial MRI from 3 males and 3 females (2 chronic LBP and 1 control for each sex) were used to demonstrate our segmentation protocol for each lumbar paraspinal muscle (erector spinae, lumbar multifidus, quadratus lumborum and psoas) and lumbar spinal level (L1-L5). Results: Proper segmentation requires an understanding of the anatomy of paraspinal lumbar muscles and the variations in paraspinal muscle morphology and composition due to age, sex, and the presence of LBP or related spinal pathologies. Other challenges in segmentation includes the presence and variations of intramuscular and epimuscular fat, and side-to-side asymmetry. Conclusion: The growing interest to assess the lumbar musculature and its role in the development and recurrence of LBP prompted the need for comprehensive and easy-to-follow resources, such as the PILLAR project to reduce inconsistencies in segmentation protocols. Standardizing manual muscle measurements from MRI will facilitate comparisons between studies while the field is progressively moving towards the automatization of paraspinal muscle measurements for large cohort studies. [ABSTRACT FROM AUTHOR]

• What is the primary question addressed by this study? The study addressed a question whether hippocampal volume (HV), a key imaging marker in dementia prediction, is associated with dementia risk but possibly with aging-related cognitive decline without dementia in general population. • What is the main finding of this study? Reduced HV is associated with higher dementia risk but predicts also cognitive decline without dementia Associations of HV with cognitive performance are method-dependent. • What is the meaning of the finding? This study raises the question of the relevance of one HV measurement as an early marker of dementia in the general population. Hippocampal volume (HV) is a key imaging marker to improve Alzheimer's disease risk prediction. However, longitudinal studies are rare, and hippocampus may also be implicated in the subtle aging-related cognitive decline observed in dementia-free individuals. Our aim was to determine whether HV, measured by manual or automatic segmentation, is associated with dementia risk and cognitive decline in participants with and without incident dementia. At baseline, 510 dementia-free participants from the French longitudinal ESPRIT cohort underwent magnetic resonance imaging. HV was measured by manual and by automatic segmentation (FreeSurfer 6.0). The presence of dementia and cognitive functions were investigated at each follow-up (2, 4, 7, 10, 12, and 15 years). Cox proportional hazards models and linear mixed models were used to assess the association of HV with dementia risk and with cognitive decline, respectively. During the 15-years follow-up, 42 participants developed dementia. Reduced HV (regardless of the measurement method) was significantly associated with higher dementia risk and cognitive decline in the whole sample. However, only the automatically measured HV was associated with cognitive decline in dementia-free participants. These results suggest that HV can be used to predict the long-term risk of dementia but also cognitive decline in a dementia-free population. This raises the question of the relevance of HV measurement as an early marker of dementia in the general population. [ABSTRACT FROM AUTHOR]

The pain pathomechanism of chronic low back pain (LBP) is complex and the available diagnostic methods are insufficient. Patients present morphological changes in volume and cross-sectional area (CSA) of lumbosacral region. The main objective of this study was to assess if CSA measurements of pelvic muscle will indicate muscle atrophy between asymptomatic and symptomatic sides in chronic LBP patients, as well as between right and left sides in healthy volunteers. In addition, inter-rater reliability for CSA measurements was examined. The study involved 71 chronic LBP patients and 29 healthy volunteers. The CSA of gluteus maximus, medius, minimus and piriformis were measured using the MRI manual segmentation method. Muscle atrophy was confirmed in gluteus maximus, gluteus minimus and piriformis muscle for over 50% of chronic LBP patients (p < 0.05). Gluteus medius showed atrophy in patients with left side pain occurrence (p < 0.001). Muscle atrophy occurred on the symptomatic side for all inspected muscles, except gluteus maximus in rater one assessment. The reliability of CSA measurements between raters calculated using CCC and ICC presented great inter-rater reproducibility for each muscle both in patients and healthy volunteers (p < 0.95). Therefore, there is the possibility of using CSA assessment in the diagnosis of patients with symptoms of chronic LBP. [ABSTRACT FROM AUTHOR]

Purpose Carotid vessel wall volume (VWV) measurement on three-dimensional ultrasonography (3DUS) outperforms conventional two-dimensional ultrasonography for carotid atherosclerosis evaluation. Although time-saving semi-automated algorithms have been introduced, their clinical availability remains limited due to a lack of validation, particularly an extensive reliability analysis. This study compared inter-observer and intra-observer reliability between manual segmentation and semi-automated segmentation for carotid VWV measurements on 3DUS. Methods Thirty-one 3DUS volume datasets were prospectively acquired from 20 healthy subjects, aged >18 years, without previous stroke, transient ischemic attack, or cardiovascular disease. Five observers segmented all volume datasets both manually and semi-automatically. The process was repeated five times. Reliability was expressed by the intraclass correlation coefficient, supplemented by the coefficient of variation. Results Carotid VWV measurements using the common carotid artery (CCA) were more reliable than those using the internal carotid artery (ICA) or external carotid artery (ECA) for both manual and semiautomated segmentation (manual segmentation, CCA: inter-observer, 0.935; intra-observer, 0.934 to 0.966; ICA: inter-observer, 0.784; intra-observer, 0.756 to 0.878; ECA: inter-observer, 0.732; intraobserver, 0.919 to 0.962; semi-automated segmentation, CCA: inter-observer, 0.986; intra-observer, 0.954 to 0.993; ICA: inter-observer, 0.977; intra-observer, 0.958 to 0.978; ECA: inter-observer, 0.966; intra-observer, 0.884 to 0.937). Total carotid VWV measurements by manual (inter-observer, 0.922; intra-observer, 0.927 to 0.961) and semi-automated segmentation (inter-observer, 0.987; intra-observer, 0.968 to 0.989) were highly reliable. Semi-automated segmentation showed higher reliability than manual segmentation for both individual and total carotid VWV measurements. Conclusion 3DUS carotid VWV measurements of the CCA are more reliable than measurements of the ICA and ECA. Total carotid VWV measurements are highly reliable. Semi-automated segmentation has higher reliability than manual segmentation.

The choroid plexus (ChP) is part of the blood‐cerebrospinal fluid barrier, regulating brain homeostasis and the brain's response to peripheral events. Its upregulation and enlargement are considered essential in psychosis. However, the timing of the ChP enlargement has not been established. This study introduces a novel magnetic resonance imaging‐based segmentation method to examine ChP volumes in two cohorts of individuals with psychosis. The first sample consists of 41 individuals with early course psychosis (mean duration of illness = 1.78 years) and 30 healthy individuals. The second sample consists of 30 individuals with chronic psychosis (mean duration of illness = 7.96 years) and 34 healthy individuals. We utilized manual segmentation to measure ChP volumes. We applied ANCOVAs to compare normalized ChP volumes between groups and partial correlations to investigate the relationship between ChP, LV volumes, and clinical characteristics. Our segmentation demonstrated good reliability (.87). We further showed a significant ChP volume increase in early psychosis (left: p <.00010, right: p <.00010) and a significant positive correlation between higher ChP and higher LV volumes in chronic psychosis (left: r =.54, p =.0030, right: r =.68; p <.0010). Our study suggests that ChP enlargement may be a marker of acute response around disease onset. It might also play a modulatory role in the chronic enlargement of lateral ventricles, often reported in psychosis. Future longitudinal studies should investigate the dynamics of ChP enlargement as a promising marker for novel therapeutic strategies. [ABSTRACT FROM AUTHOR]

Introduction: Accurate and precise delineation of the globus pallidus pars interna (GPi) and subthalamic nucleus (STN) is critical for the clinical treatment and research of Parkinson's disease (PD). Automated segmentation is a developing technology which addresses limitations of visualizing deep nuclei on MR imaging and standardizing their definition in research applications. We sought to compare manual segmentation with three workflows for template-to-patient nonlinear registration providing atlas-based automatic segmentation of deep nuclei. Methods: Bilateral GPi, STN, and red nucleus (RN) were segmented for 20 PD and 20 healthy control (HC) subjects using 3T MRIs acquired for clinical purposes. The automated workflows used were an option available in clinical practice and two common research protocols. Quality control (QC) was performed on registered templates via visual inspection of readily discernible brain structures. Manual segmentation using T1, proton density, and T2 sequences was used as "ground truth" data for comparison. Dice similarity coefficient (DSC) was used to assess agreement between segmented nuclei. Further analysis was done to compare the influences of disease state and QC classifications on DSC. Results: Automated segmentation workflows (CIT-S, CRV-AB, and DIST-S) had the highest DSC for the RN and lowest for the STN. Manual segmentations outperformed automated segmentation for all workflows and nuclei; however, for 3/9 workflows (CIT-S STN, CRV-AB STN, and CRV-AB GPi) the differences were not statically significant. HC and PD only showed significant differences in 1/9 comparisons (DIST-S GPi). QC classification only demonstrated significantly higher DSC in 2/9 comparisons (CRV-AB RN and GPi). Conclusion: Manual segmentations generally performed better than automated segmentations. Disease state does not appear to have a significant effect on the quality of automated segmentations via nonlinear template-to-patient registration. Notably, visual inspection of template registration is a poor indicator of the accuracy of deep nuclei segmentation. As automatic segmentation methods continue to evolve, efficient and reliable QC methods will be necessary to support safe and effective integration into clinical workflows. [ABSTRACT FROM AUTHOR]

The manual segmentation of muscles on magnetic resonance images is the gold standard procedure to reconstruct muscle volumes from medical imaging data and extract critical information for clinical and research purposes. (Semi)automatic methods have been proposed to expedite the otherwise lengthy process. These, however, rely on manual segmentations. Nonetheless, the repeatability of manual muscle volume segmentations performed on clinical MRI data has not been thoroughly assessed. When conducted, volumetric assessments often disregard the hip muscles. Therefore, one trained operator performed repeated manual segmentations (n = 3) of the iliopsoas (n = 34) and gluteus medius (n = 40) muscles on coronal T1-weighted MRI scans, acquired on 1.5 T scanners on a clinical population of patients elected for hip replacement surgery. Reconstructed muscle volumes were divided in sub-volumes and compared in terms of volume variance (normalized variance of volumes – nVV), shape (Jaccard Index—JI) and surface similarity (maximal Hausdorff distance—HD), to quantify intra-operator repeatability. One-way repeated measures ANOVA (or equivalent) tests with Bonferroni corrections for multiple comparisons were conducted to assess statistical significance. For both muscles, repeated manual segmentations were highly similar to one another (nVV: 2–6%, JI > 0.78, HD < 15 mm). However, shape and surface similarity were significantly lower when muscle extremities were included in the segmentations (e.g., iliopsoas: HD –12.06 to 14.42 mm, P < 0.05). Our findings show that the manual segmentation of hip muscle volumes on clinical MRI scans provides repeatable results over time. Nonetheless, extreme care should be taken in the segmentation of muscle extremities. [ABSTRACT FROM AUTHOR]

Manual quantification of the hippocampal atrophy state and rate is time consuming and prone to poor reproducibility, even when performed by neuroanatomical experts. The automation of hippocampal segmentation has been investigated in normal aging, epilepsy, and in Alzheimer's disease. Our first goal was to compare manual and automated hippocampal segmentation in ischemic stroke and to, secondly, study the impact of stroke lesion presence on hippocampal volume estimation. We used eight automated methods to segment T1-weighted MR images from 105 ischemic stroke patients and 39 age-matched controls sampled from the Cognition And Neocortical Volume After Stroke (CANVAS) study. The methods were: AdaBoost, Atlas-based Hippocampal Segmentation (ABHS) from the IDeALab, Computational Anatomy Toolbox (CAT) using 3 atlas variants (Hammers, LPBA40 and Neuromorphometics), FIRST, FreeSurfer v5.3, and FreeSurfer v6.0-Subfields. A number of these methods were employed to re-segment the T1 images for the stroke group after the stroke lesions were masked (i.e., removed). The automated methods were assessed on eight measures: process yield (i.e. segmentation success rate), correlation (Pearson's R and Shrout's ICC), concordance (Lin's RC and Kandall's W), slope 'a' of best-fit line from correlation plots, percentage of outliers from Bland-Altman plots, and significance of control-stroke difference. We eliminated the redundant measures after analysing between-measure correlations using Spearman's rank correlation. We ranked the automated methods based on the sum of the remaining non-redundant measures where each measure ranged between 0 and 1. Subfields attained an overall score of 96.3%, followed by AdaBoost (95.0%) and FIRST (94.7%). CAT using the LPBA40 atlas inflated hippocampal volumes the most, while the Hammers atlas returned the smallest volumes overall. FIRST (p = 0.014), FreeSurfer v5.3 (p = 0.007), manual tracing (p = 0.049), and CAT using the Neuromorphometics atlas (p = 0.017) all showed a significantly reduced hippocampal volume mean for the stroke group compared to control at three months. Moreover, masking of the stroke lesions prior to segmentation resulted in hippocampal volumes which agreed less with manual tracing. These findings recommend an automated segmentation without lesion masking as a more reliable procedure for the estimation of hippocampal volume in ischemic stroke.

Abstract Background Measures of hip muscle morphology and composition (e.g., muscle size and fatty infiltration) are possible with magnetic resonance imaging (MRI). Standardised protocols or guidelines do not exist for evaluation of hip muscle characteristics, hindering reliable and valid inter-study analysis. This scoping review aimed to collate and synthesise MRI methods for measuring lateral hip muscle size and fatty infiltration to inform the future development of standardised protocols. Methods Five electronic databases (Medline, CINAHL, Embase, SportsDISCUS and AMED) were searched. Healthy or musculoskeletal pain populations that used MRI to assess lateral hip muscle size and fatty infiltration were included. Lateral hip muscles of interest included tensor fascia late (TFL), gluteus maximus, gluteus medius, and gluteus minimus. Data on MRI parameters, axial slice location, muscle size and fatty infiltrate measures were collected and analysed. Cross referencing for anatomical locations were made between MRI axial slice and E-12 anatomical plastinate sections. Results From 2684 identified publications, 78 studies contributed data on volume (n = 31), cross sectional area (CSA) (n = 24), and fatty infiltration (n = 40). Heterogeneity was observed for MRI parameters and anatomical boundaries scrutinizing hip muscle size and fatty infiltration. Seven single level axial slices were identified that provided consistent CSA measurement, including three for both gluteus maximus and TFL, and four for both gluteus medius and minimus. For assessment of fatty infiltration, six axial slice locations were identified including two for TFL, and four for each of the gluteal muscles. Conclusions Several consistent anatomical levels were identified for single axial MR slice to facilitate muscle size and fatty infiltration muscle measures at the hip, providing the basis for reliable and accurate data synthesis and improvements in the validity of future between studies analyses. This work establishes the platform for standardised methods for the MRI assessment of lateral hip musculature and will aid in the examination of musculoskeletal conditions around the hip joint. Further studies into whole muscle measures are required to further optimise methodological parameters for hip muscle assessment.

PurposeGeographic atrophy (GA) is the sequelae of macular degeneration. Automated inner retinal analysis using optical coherence tomography is flawed because segmentation software is calibrated for normal eyes. The purpose of this study is to determine whether ganglion cell layer (GCL) volume is reduced in GA using manual analysis.MethodsNineteen eyes with subfoveal GA and 22 controls were selected for morphometric analyses. Heidelberg scanning laser ophthalmoscope optical coherence tomography images of the optic nerve and macula were obtained, and the Viewing Module was used to manually calibrate retinal layer segmentation. Retinal layer volumes in the central 3-mm and surrounding 6-mm diameter were measured. Linear mixed models were used for statistics.ResultsThe GCL volume in the central 3 mm of the macula is less (P = 0.003), and the retinal nerve fiber layer volume is more (P = 0.02) in patients with GA when compared with controls. Ganglion cell layer volume positively correlated with outer nuclear layer volume (P = 0.020).ConclusionThe patients with geographic atrophy have a small significant loss of the GCL. Ganglion cell death may precede axonal loss, and increased macular retinal nerve fiber layer volumes are not indicative of GCL volume. Residual ganglion cell stimulation by interneurons may enable vision in patients with GA.

Cervical cancer is the most common cancer and ranked as 4th in morbidity and mortality among Malaysian women. Currently, Magnetic Resonance Imaging (MRI) is considered as the gold standard imaging modality for tumours with a stage higher than IB2, due to its superiority in diagnostic assessment of tumour infiltration with excellent soft-tissue contrast. In this research, the robustness of semi-automatic segmentation has been evaluated using a flood-fill algorithm for quantitative feature extraction, using 30 diffusion weighted MRI images (DWI-MRI) of cervical cancer patients. The relevant features were extracted from DWI-MRI segmented images of cervical cancer. First order statistics, shape features, and textural features were extracted and analysed. The intra-class relation coefficient (ICC) was used to compare 662 radiomic features extracted from manual and semi-automatic segmentations. Notably, the features extracted from the semi-automatic segmentation and flood filling algorithm (average ICC = 0.952 0.009, p > 0.05) were significantly higher than the manual extracted features (average ICC = 0.897 0.011, p > 0.05). Henceforth, we demonstrate that the semi-automatic segmentation is slightly expanded to manual segmentation as it produces more robust and reproducible radiomic features. [ABSTRACT FROM AUTHOR]

Although automated methods for stroke lesion segmentation exist, many researchers still rely on manual segmentation as the gold standard. Our detailed, standardized protocol for stroke lesion tracing on high-resolution 3D T1-weighted (T1w) magnetic resonance imaging (MRI) has been used to trace over 1,300 stroke MRI. In the current study, we describe the protocol, including a step-by-step method utilized for training multiple individuals to trace lesions (“tracers”) in a consistent manner and suggestions for distinguishing between lesioned and non-lesioned areas in stroke brains. Inter-rater and intra-rater reliability were calculated across six tracers trained using our protocol, resulting in an average intraclass correlation of 0.98 and 0.99, respectively, as well as a Dice similarity coefficient of 0.727 and 0.839, respectively. This protocol provides a standardized guideline for researchers performing manual lesion segmentation in stroke T1-weighted MRI, with detailed methods to promote reproducibility in stroke research.

Simple Summary: Tumor segmentation is a key step in oncologic imaging processing and is a time-consuming process usually performed manually by radiologists. To facilitate it, there is growing interest in applying deep-learning segmentation algorithms. Thus, we explore the variability between two observers performing manual segmentation and use the state-of-the-art deep learning architecture nnU-Net to develop a model to detect and segment neuroblastic tumors on MR images. We were able to show that the variability between nnU-Net and manual segmentation is similar to the inter-observer variability in manual segmentation. Furthermore, we compared the time needed to manually segment the tumors from scratch with the time required for the automatic model to segment the same cases, with posterior human validation with manual adjustment when needed. Tumor segmentation is one of the key steps in imaging processing. The goals of this study were to assess the inter-observer variability in manual segmentation of neuroblastic tumors and to analyze whether the state-of-the-art deep learning architecture nnU-Net can provide a robust solution to detect and segment tumors on MR images. A retrospective multicenter study of 132 patients with neuroblastic tumors was performed. Dice Similarity Coefficient (DSC) and Area Under the Receiver Operating Characteristic Curve (AUC ROC) were used to compare segmentation sets. Two more metrics were elaborated to understand the direction of the errors: the modified version of False Positive (FPRm) and False Negative (FNR) rates. Two radiologists manually segmented 46 tumors and a comparative study was performed. nnU-Net was trained-tuned with 106 cases divided into five balanced folds to perform cross-validation. The five resulting models were used as an ensemble solution to measure training (n = 106) and validation (n = 26) performance, independently. The time needed by the model to automatically segment 20 cases was compared to the time required for manual segmentation. The median DSC for manual segmentation sets was 0.969 (±0.032 IQR). The median DSC for the automatic tool was 0.965 (±0.018 IQR). The automatic segmentation model achieved a better performance regarding the FPRm. MR images segmentation variability is similar between radiologists and nnU-Net. Time leverage when using the automatic model with posterior visual validation and manual adjustment corresponds to 92.8%. [ABSTRACT FROM AUTHOR]

Background: Automated magnetic resonance imaging (MRI) volumetry is a promising tool to evaluate regional brain volumes in dementia and especially Alzheimer's disease (AD). Purpose: To compare automated methods and the gold standard manual segmentation in measuring regional brain volumes on MRI across healthy controls, patients with mild cognitive impairment, and patients with dementia due to AD. Study Type: Systematic review and meta‐analysis. Data Sources: MEDLINE, Embase, and PsycINFO were searched through October 2021. Field Strength: 1.0 T, 1.5 T, or 3.0 T. Assessment: Two review authors independently identified studies for inclusion and extracted data. Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS‐2). Statistical Tests: Standardized mean differences (SMD; Hedges' g) were pooled using random‐effects meta‐analysis with robust variance estimation. Subgroup analyses were undertaken to explore potential sources of heterogeneity. Sensitivity analyses were conducted to examine the impact of the within‐study correlation between effect estimates on the meta‐analysis results. Results: Seventeen studies provided sufficient data to evaluate the hippocampus, lateral ventricles, and parahippocampal gyrus. The pooled SMD for the hippocampus, lateral ventricles, and parahippocampal gyrus were 0.22 (95% CI −0.50 to 0.93), 0.12 (95% CI −0.13 to 0.37), and −0.48 (95% CI −1.37 to 0.41), respectively. For the hippocampal data, subgroup analyses suggested that the pooled SMD was invariant across clinical diagnosis and field strength. Subgroup analyses could not be conducted on the lateral ventricles data and the parahippocampal gyrus data due to insufficient data. The results were robust to the selected within‐study correlation value. Data Conclusion: While automated methods are generally comparable to manual segmentation for measuring hippocampal, lateral ventricle, and parahippocampal gyrus volumes, wide 95% CIs and large heterogeneity suggest that there is substantial uncontrolled variance. Thus, automated methods may be used to measure these regions in patients with AD but should be used with caution. Evidence Level: 3 Technical Efficacy: Stage 3 [ABSTRACT FROM AUTHOR]

The pain pathomechanism of chronic low back pain (LBP) is complex and the available diagnostic methods are insufficient. Patients present morphological changes in volume and cross-sectional area (CSA) of lumbosacral region. The main objective of this study was to assess if CSA measurements of pelvic muscle will indicate muscle atrophy between asymptomatic and symptomatic sides in chronic LBP patients, as well as between right and left sides in healthy volunteers. In addition, inter-rater reliability for CSA measurements was examined. The study involved 71 chronic LBP patients and 29 healthy volunteers. The CSA of gluteus maximus, medius, minimus and piriformis were measured using the MRI manual segmentation method. Muscle atrophy was confirmed in gluteus maximus, gluteus minimus and piriformis muscle for over 50% of chronic LBP patients (p < 0.05). Gluteus medius showed atrophy in patients with left side pain occurrence (p < 0.001). Muscle atrophy occurred on the symptomatic side for all inspected muscles, except gluteus maximus in rater one assessment. The reliability of CSA measurements between raters calculated using CCC and ICC presented great inter-rater reproducibility for each muscle both in patients and healthy volunteers (p < 0.95). Therefore, there is the possibility of using CSA assessment in the diagnosis of patients with symptoms of chronic LBP.

Background: Measures of hip muscle morphology and composition (e.g., muscle size and fatty infiltration) are possible with magnetic resonance imaging (MRI). Standardised protocols or guidelines do not exist for evaluation of hip muscle characteristics, hindering reliable and valid inter-study analysis. This scoping review aimed to collate and synthesise MRI methods for measuring lateral hip muscle size and fatty infiltration to inform the future development of standardised protocols.Methods: Five electronic databases (Medline, CINAHL, Embase, SportsDISCUS and AMED) were searched. Healthy or musculoskeletal pain populations that used MRI to assess lateral hip muscle size and fatty infiltration were included. Lateral hip muscles of interest included tensor fascia late (TFL), gluteus maximus, gluteus medius, and gluteus minimus. Data on MRI parameters, axial slice location, muscle size and fatty infiltrate measures were collected and analysed. Cross referencing for anatomical locations were made between MRI axial slice and E-12 anatomical plastinate sections.Results: From 2684 identified publications, 78 studies contributed data on volume (n = 31), cross sectional area (CSA) (n = 24), and fatty infiltration (n = 40). Heterogeneity was observed for MRI parameters and anatomical boundaries scrutinizing hip muscle size and fatty infiltration. Seven single level axial slices were identified that provided consistent CSA measurement, including three for both gluteus maximus and TFL, and four for both gluteus medius and minimus. For assessment of fatty infiltration, six axial slice locations were identified including two for TFL, and four for each of the gluteal muscles.Conclusions: Several consistent anatomical levels were identified for single axial MR slice to facilitate muscle size and fatty infiltration muscle measures at the hip, providing the basis for reliable and accurate data synthesis and improvements in the validity of future between studies analyses. This work establishes the platform for standardised methods for the MRI assessment of lateral hip musculature and will aid in the examination of musculoskeletal conditions around the hip joint. Further studies into whole muscle measures are required to further optimise methodological parameters for hip muscle assessment. [ABSTRACT FROM AUTHOR]

The article addresses the issue of developing of software tools for manual segmentation of tomography images supporting radiologist’s personal content. Through an expert survey we have identified the requirements for such software tools from the doctors’ point of view as end users, as well as to the nomenclature and functionality of tools that implement these requirements. In order to meet the identified requirements, we have developed a solution based on a client-server architecture with a cloud access point. The nomenclature of tools for marking tomographic images implemented in the solution, as well as the methodology for working with them, fully complies with the identified requirements. The following functions have been developed and implemented: calculation of the volume of the region of interest, as well as three options for the semiautomatic segmentation of the image based on threshold, extreme points and neuron networks. All functions have customizable parameters and (or) implementation options, which provides flexibility in solving specific markup problems. Experimental studies have shown that the constructed service meets all the requirements put forward by radiologists and corresponds to the global level in terms of accuracy and speed (performance) of segmentation.

Cervical cancer is the most common cancer and ranked as 4th in morbidity and mortality among Malaysian women. Currently, Magnetic Resonance Imaging (MRI) is considered as the gold standard imaging modality for tumours with a stage higher than IB2, due to its superiority in diagnostic assessment of tumour infiltration with excellent soft-tissue contrast. In this research, the robustness of semi-automatic segmentation has been evaluated using a flood-fill algorithm for quantitative feature extraction, using 30 diffusion weighted MRI images (DWI-MRI) of cervical cancer patients. The relevant features were extracted from DWI-MRI segmented images of cervical cancer. First order statistics, shape features, and textural features were extracted and analysed. The intra-class relation coefficient (ICC) was used to compare 662 radiomic features extracted from manual and semi-automatic segmentations. Notably, the features extracted from the semi-automatic segmentation and flood filling algorithm (average ICC = 0.952 0.009, p > 0.05) were significantly higher than the manual extracted features (average ICC = 0.897 0.011, p > 0.05). Henceforth, we demonstrate that the semi-automatic segmentation is slightly expanded to manual segmentation as it produces more robust and reproducible radiomic features.

Purpose: Accurate identification of metastatic lesions is important for improvement in biomechanical models that calculate the fracture risk of metastatic bones. The aim of this study was therefore to assess the inter- and intra-operator reliability of manual segmentation of femoral metastatic lesions. Methods: CT scans of 54 metastatic femurs (19 osteolytic, 17 osteoblastic, and 18 mixed) were segmented two times by two operators. Dice coefficients (DCs) were calculated adopting the quantification that a DC˃0.7 indicates good reliability. Results: Generally, rather poor inter- and intra-operator reliability of lesion segmentation were found. Inter-operator DCs were 0.54 (± 0.28) and 0.50 (± 0.32) for the first and second segmentations, respectively, whereas intra-operator DCs were 0.56 (± 0.28) for operator I and 0.71 (± 0.23) for operator II. Larger lesions scored significantly higher DCs in comparison with smaller lesions. Of the femurs with larger mean segmentation volumes, 83% and 93% were segmented with good inter- and intra-operator DCs (> 0.7), respectively. There was no difference between the mean DCs of osteolytic, osteoblastic, and mixed lesions. Conclusion: Manual segmentation of femoral bone metastases is very challenging and resulted in unsatisfactory mean reliability values. There is a need for development of a segmentation protocol to reduce the inter- and intra-operator segmentation variation as the first step and use of computer-assisted segmentation tools as a second step as this study shows that manual segmentation of femoral metastatic lesions is highly challenging. [ABSTRACT FROM AUTHOR]

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BackgroundThis study aimed to have international experts converge on a harmonized definition of whole hippocampus boundaries and segmentation procedures, to define standard operating procedures for magnetic resonance (MR)-based manual hippocampal segmentation.MethodsThe panel received a questionnaire regarding whole hippocampus boundaries and segmentation procedures. Quantitative information was supplied to allow evidence-based answers. A recursive and anonymous Delphi procedure was used to achieve convergence. Significance of agreement among panelists was assessed by exact probability on Fisher's and binomial tests.ResultsAgreement was significant on the inclusion of alveus/fimbria (P = .021), whole hippocampal tail (P = .013), medial border of the body according to visible morphology (P = .0006), and on this combined set of features (P = .001). This definition captures 100% of hippocampal tissue, 100% of Alzheimer's disease-related atrophy, and demonstrated good reliability on preliminary intrarater (0.98) and inter-rater (0.94) estimates.DiscussionConsensus was achieved among international experts with respect to hippocampal segmentation using MR resulting in a harmonized segmentation protocol.

BackgroundAn international Delphi panel has defined a harmonized protocol (HarP) for the manual segmentation of the hippocampus on MR. The aim of this study is to study the concurrent validity of the HarP toward local protocols, and its major sources of variance.MethodsFourteen tracers segmented 10 Alzheimer's Disease Neuroimaging Initiative (ADNI) cases scanned at 1.5 T and 3T following local protocols, qualified for segmentation based on the HarP through a standard web-platform and resegmented following the HarP. The five most accurate tracers followed the HarP to segment 15 ADNI cases acquired at three time points on both 1.5 T and 3T.ResultsThe agreement among tracers was relatively low with the local protocols (absolute left/right ICC 0.44/0.43) and much higher with the HarP (absolute left/right ICC 0.88/0.89). On the larger set of 15 cases, the HarP agreement within (left/right ICC range: 0.94/0.95 to 0.99/0.99) and among tracers (left/right ICC range: 0.89/0.90) was very high. The volume variance due to different tracers was 0.9% of the total, comparing favorably to variance due to scanner manufacturer (1.2), atrophy rates (3.5), hemispheric asymmetry (3.7), field strength (4.4), and significantly smaller than the variance due to atrophy (33.5%, P < .001), and physiological variability (49.2%, P < .001).ConclusionsThe HarP has high measurement stability compared with local segmentation protocols, and good reproducibility within and among human tracers. Hippocampi segmented with the HarP can be used as a reference for the qualification of human tracers and automated segmentation algorithms.

Introduction: Muscle diffusion tensor imaging (mDTI) is a quantitative MRI technique that can provide information about muscular microstructure and integrity. Ultrasound and DTI studies have shown intramuscular differences, and therefore separation of different muscles for analysis is essential. The commonly used methods to assess DTI metrics in muscles are manual segmentation and tract‐based analysis. Recently methods such as volume‐based tractography have been applied to optimize muscle architecture estimation, but can also be used to assess DTI metrics. Purpose To evaluate diffusion metrics obtained using three different methods—volume‐based tractography, manual segmentation‐based analysis and tract‐based analysis—with respect to their interrater reliability and their ability to detect intramuscular variance. Materials and methods: 30 volunteers underwent an MRI examination in a 3 T scanner using a 16‐channel Torso XL coil. Diffusion‐weighted images were acquired to obtain DTI metrics. These metrics were evaluated in six thigh muscles using volume‐based tractography, manual segmentation and standard tractography. All three methods were performed by two independent raters to assess interrater reliability by ICC analysis and Bland‐Altman plots. Ability to assess intramuscular variance was compared using an ANOVA with muscle as a between‐subjects factor. Results: Interrater reliability for all methods was found to be excellent. The highest interrater reliability was found for volume‐based tractography (ICC ≥ 0.967). Significant differences for the factor muscle in all examined diffusion parameters were shown in muscles using all methods (main effect p < 0.001). Conclusions: Diffusion data can be assessed by volume tractography, standard tractography and manual segmentation with high interrater reliability. Each method produces different results for the investigated DTI parameters. Volume‐based tractography was superior to conventional manual segmentation and tractography regarding interrater reliability and detection of intramuscular variance, while tract‐based analysis showed the lowest coefficients of variation. [ABSTRACT FROM AUTHOR]

Background: Diagnosis of hip joint plays an important role in early screening of hip diseases such as coxarthritis, heterotopic ossification, osteonecrosis of the femoral head, etc. Early detection of hip dysplasia on X-ray films may probably conduce to early treatment of patients, which can help to cure patients or relieve their pain as much as possible. There has been no method or tool for automatic diagnosis of hip dysplasia till now.Results: A semi-automatic method for diagnosis of hip dysplasia is proposed. Considering the complexity of medical imaging, the contour of acetabulum, femoral head, and the upper side of thigh-bone are manually marked. Feature points are extracted according to marked contours. Traditional knowledge-driven diagnostic criteria is abandoned. Instead, a data-driven diagnostic model for hip dysplasia is presented. Angles including CE, sharp, and Tonnis angle which are commonly measured in clinical diagnosis, are automatically obtained. Samples, each of which consists of these three angle values, are used for clustering according to their densities in a descending order. A three-dimensional normal distribution derived from the cluster is built and regarded as the parametric model for diagnosis of hip dysplasia. Experiments on 143 X-ray films including 286 samples (i.e., 143 left and 143 right hip joints) demonstrate the effectiveness of our method. According to the method, a computer-aided diagnosis tool is developed for the convenience of clinicians, which can be downloaded at http://www.bio-nefu.com/HIPindex/. The data used to support the findings of this study are available from the corresponding authors upon request.Conclusions: This data-driven method provides a more objective measurement of the angles. Besides, it provides a new criterion for diagnosis of hip dysplasia other than doctors' experience deriving from knowledge-driven clinical manual, which actually corresponds to very different way for clinical diagnosis of hip dysplasia.

Abstract Objective MRI derived spinal‐muscle morphology measurements have potential diagnostic, prognostic, and therapeutic applications in spinal health. Muscle morphology in the thoracic spine is an important determinant of kyphosis severity in older adults. However, the literature on quantification of spinal muscles to date has been limited to cervical and lumbar regions. Hence, we aim to propose a method to quantitatively identify regions of interest of thoracic spinal muscle in axial MR images and investigate the repeatability of their measurements. Methods Middle (T4‐T5) and lower (T8‐T9) thoracic levels of six healthy volunteers (age 26 ± 6 years) were imaged in an upright open scanner (0.5T MROpen, Paramed, Genoa, Italy). A descriptive methodology for defining the regions of interest of trapezius, erector spinae, and transversospinalis in axial MR images was developed. The guidelines for segmentation are laid out based on the points of origin and insertion, probable size, shape, and the position of the muscle groups relative to other recognizable anatomical landmarks as seen from typical axial MR images. 2D parameters such as muscle cross‐sectional area (CSA) and muscle position (radius and angle) with respect to the vertebral body centroid were computed and 3D muscle geometries were generated. Intra and inter‐rater segmentation repeatability was assessed with intraclass correlation coefficient (ICC (3,1)) for 2D parameters and with dice coefficient (DC) for 3D parameters. Results Intra and inter‐rater repeatability for 2D and 3D parameters for all muscles was generally good/excellent (average ICC (3,1) = 0.9 with ranges of 0.56‐0.98; average DC = 0.92 with ranges from 0.85‐0.95). Conclusion The guidelines proposed are important for reliable MRI‐based measurements and allow meaningful comparisons of muscle morphometry in the thoracic spine across different studies globally. Good segmentation repeatability suggests we can further investigate the effect of posture and spinal curvature on muscle morphology in the thoracic spine.

The aims of this study were to evaluate the intra- and interobserver reproducibility of manual segmentation of bone sarcomas in magnetic resonance imaging (MRI) studies and to compare manual and semiautomatic segmentation methods. This retrospective study included twelve osteosarcoma and eight Ewing sarcoma MRI studies performed prior to any therapeutic intervention. All cases were histopathologically confirmed. Three radiologists used 3D-Slicer software to perform manual segmentation of bone sarcomas in a blinded and independent manner. One radiologist segmented manually and also performed semiautomatic segmentation with the GrowCut tool. Segmentation exercises were timed for comparison. The dice similarity coefficient (DSC) and Hausdorff distance (HD) were used to evaluate similarity between the segmentation results and further statistical analyses were performed to compare DSC, HD, and volumetric results. Manual segmentation was reproducible with intraobserver DSC varying from 0.83 to 0.97 and HD from 3.37 to 28.73 mm. Interobserver DSC of manual segmentation showed variation from 0.73 to 0.97 and HD from 3.93 to 33.40 mm. Semiautomatic segmentation compared to manual segmentation resulted in DSCs of 0.71−0.96 and HDs of 5.38−31.54 mm. Semiautomatic segmentation required significantly less time compared to manual segmentation (P value ≤0.05). Among all situations compared, tumor volumetry did not show significant statistical differences (P value >0.05). We found excellent intra- and interobserver agreement for manual segmentation of osteosarcoma and Ewing sarcoma. There was high similarity between manual and semiautomatic segmentation, with a significant reduction of segmentation time using the semiautomatic method.

Abstract Background It has been suggested that the quantification of paravertebral muscle composition and morphology (e.g. size/shape/structure) with magnetic resonance imaging (MRI) has diagnostic, prognostic, and therapeutic potential in contributing to overall musculoskeletal health. If this is to be realised, then consensus towards standardised MRI methods for measuring muscular size/shape/structure are crucial to allow the translation of such measurements towards management of, and hopefully improved health for, those with some musculoskeletal conditions. Following on from an original paper detailing methods for measuring muscles traversing the lumbar spine, we propose new methods based on anatomical cross-reference that strive towards standardising MRI-based quantification of anterior and posterior cervical spine muscle composition. Methods In this descriptive technical advance paper we expand our methods from the lumbar spine by providing a detailed examination of regional cervical spine muscle morphology, followed by a comprehensive description of the proposed technique defining muscle ROI from axial MRI. Cross-referencing cervical musculature and vertebral anatomy includes an innovative comparison between axial E12 sheet-plastinates derived from cadaveric material to a series of axial MRIs detailing commonly used sequences. These images are shown at different cervical levels to illustrate differences in regional morphology. The method for defining ROI for both anterior (scalenes group, sternocleidomastoid, longus colli, longus capitis) and posterior (multifidus, semispinalis cervicis, semispinalis capitis, splenius capitis) cervical muscles is then described and discussed in relation to existing literature. Results A series of steps towards standardising the quantification of cervical spine muscle quality are described, with concentration on the measurement of muscle volume and fatty infiltration (MFI). We offer recommendations for imaging parameters that should additionally inform a priori decisions when planning investigations of cervical muscle tissues with MRI. Conclusions The proposed method provides an option rather than a final position for quantifying cervical spine muscle composition and morphology using MRI. We intend to stimulate discussion towards establishing measurement consensus whereby data-pooling and meaningful comparisons between imaging studies (primarily MRI) investigating cervical muscle quality becomes available and the norm.

Abstract Objectives The aim of the present study is to determine if the delineation of one or two optimally chosen intracranial areas (ICA) is enough to achieve adequate estimates of intracranial volume (ICV) in magnetic resonance imaging. Methods The correlations of 62 fully delineated ICVs with four types of ICV estimates were calculated. The estimate types were: (1) a single midsagittal ICA, (2) single ICA multiplied by the intracranial width perpendicular to the ICA, (3) sum of two ICAs multiplied by the perpendicular intracranial width and (4) shape-preserving piecewise cubic interpolation using two ICAs. For methods 2–4, the fully delineated ICVs were randomly separated into an evaluation and a validation set of equal size. Method 1 was validated against all of the fully delineated ICVs. Results Estimates from method 1 had a Pearson correlation of 0.904 with fully delineated ICV. For method 2, the correlation was 0.986 when delineating the sagittal ICA at 31% of the sagittal intracranial width. For methods 3 and 4, the correlations were both 0.997 when delineating the sagittal ICAs at 17.5 and 64% and at 12 and 64% respectively. Conclusions Delineation of two specific intracranial areas is sufficient for intracranial volume estimation. Main messages • Delineation of two specific intracranial areas is sufficient for intracranial volume estimation. • The estimates had a Pearson correlation of 0.997 with intracranial volume. • The estimation should take no more than 5 min.

Objective: MRI derived spinal‐muscle morphology measurements have potential diagnostic, prognostic, and therapeutic applications in spinal health. Muscle morphology in the thoracic spine is an important determinant of kyphosis severity in older adults. However, the literature on quantification of spinal muscles to date has been limited to cervical and lumbar regions. Hence, we aim to propose a method to quantitatively identify regions of interest of thoracic spinal muscle in axial MR images and investigate the repeatability of their measurements. Methods: Middle (T4‐T5) and lower (T8‐T9) thoracic levels of six healthy volunteers (age 26 ± 6 years) were imaged in an upright open scanner (0.5T MROpen, Paramed, Genoa, Italy). A descriptive methodology for defining the regions of interest of trapezius, erector spinae, and transversospinalis in axial MR images was developed. The guidelines for segmentation are laid out based on the points of origin and insertion, probable size, shape, and the position of the muscle groups relative to other recognizable anatomical landmarks as seen from typical axial MR images. 2D parameters such as muscle cross‐sectional area (CSA) and muscle position (radius and angle) with respect to the vertebral body centroid were computed and 3D muscle geometries were generated. Intra and inter‐rater segmentation repeatability was assessed with intraclass correlation coefficient (ICC (3,1)) for 2D parameters and with dice coefficient (DC) for 3D parameters. Results: Intra and inter‐rater repeatability for 2D and 3D parameters for all muscles was generally good/excellent (average ICC (3,1) = 0.9 with ranges of 0.56‐0.98; average DC = 0.92 with ranges from 0.85‐0.95). Conclusion: The guidelines proposed are important for reliable MRI‐based measurements and allow meaningful comparisons of muscle morphometry in the thoracic spine across different studies globally. Good segmentation repeatability suggests we can further investigate the effect of posture and spinal curvature on muscle morphology in the thoracic spine. [ABSTRACT FROM AUTHOR]

In this study, a survey of multiple sclerosis (MS) classification and segmentation process is presented, which is based on magnetic resonance imaging. Knowledge of MS lesions is gained by determining the number of sample lesions in order that the lesion development level can be followed precisely; therefore, the effects of pharmaceuticals in medical tests can be accurately assessed. Accurate recognition of MS lesions in magnetic resonance images is an additionally complex process because of their changing shapes and sizes which can be very difficult to identify based on anatomical positions in various subjects. This can be determined by precise segmentation; manual segmentation would be very difficult to perform as it requires high level knowledge which takes additional time. Inter‐ and intra‐expert variability need to be determined in order to perform the automated segmentation of lesions. The principal aim of this survey effort is to provide an analysis of the different categorization and segmentation methods and their techniques. This survey work will be valuable for researchers working in MS by considering and carefully evaluating the past work. The benefits and drawbacks of existing techniques are reviewed and the issue of MS lesion segmentation and classification is elucidated. [ABSTRACT FROM AUTHOR]

The pituitary gland (PG) is a key component of the essential endocrine systems in humans and animals, including the hypothalamic–pituitary–adrenal, hypothalamic–pituitary–gonadal, and hypothalamic–pituitary–thyroid axes. Structural changes in the PG are observed in a number of psychiatric disorders. Psychiatric disorders are typically characterized by subtle, time-dependent anatomical changes in the brain, and their study necessitates highly powered, longitudinal investigations. Structural magnetic resonance imaging (MRI) is a non-invasive technology that is ideally suited to detect changes in anatomical structures over time. In this paper, we will review the main findings on pituitary function and structure in the context of healthy development and of psychiatric disorders, with particular emphasis on MRI studies. The latter have not always succeeded in providing a clear theoretical framework of mental disorders, which may be explained by low resolution and differences in preprocessing methods, imprecise segmentation rules that do not account for the anatomical and functional specificity of the anterior and posterior lobes of the PG, and inadequate categorization of clinical subjects. We review those limitations and propose solutions for future research. [ABSTRACT FROM AUTHOR]