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Objective: To evaluate the repeatability of cartilage volume and thickness values at 1.5 T MRI using a fully automatic cartilage segmentation method and reproducibility of the method between 1.5 T and 3 T data. Methods: The study included 20 knee joints from 10 healthy subjects with each subject having undergone double-knee MRI. All knees were scanned at 1.5 T and 3 T MR scanners using a three-dimensional (3D) high-resolution dual-echo in steady state (DESS) sequence. Cartilage volume and thickness of 21 subregions were quantified using a fully automatic cartilage segmentation research application (MR Chondral Health, version 3.0, Siemens Healthcare, Erlangen, Germany). The volume and thickness values derived from fully automatically computed segmentation masks were analyzed for the scan–rescan data from the same volunteers. The accuracy of the automatic segmentation of the cartilage in 1.5 T images was evaluated by the dice similarity coefficient (DSC) and Hausdorff distance (HD) using the manually corrected segmentation as a reference. The volume and thickness values calculated from 1.5 T and 3 T were also compared. Results: No statistically significant differences were found for cartilage thickness or volume across all subregions between the scan–rescanned data at 1.5 T (P > 0.05). The mean DSC between the fully automatic and manually corrected knee cartilage segmentation contours at 1.5 T was 0.9946. The average value of HD was 2.41 mm. Overall, there was no statistically significant difference in the cartilage volume or thickness in most-subregions between the two field strengths (P > 0.05) except for the medial region of femur and tibia. Bland–Altman plot and intraclass correlation coefficient (ICC) showed high consistency between results obtained based on same and different scanning sequences. Conclusion: The cartilage segmentation software had high repeatability for DESS images obtained from the same device. In addition, the overall reproducibility of the images obtained from equipment of two different field strengths was satisfactory. [ABSTRACT FROM AUTHOR]

Background and objective: 3D rotational angiography (3DRA) provides high quality images of the cerebral arteriovenous malformation (AVM) nidus that can be reconstructed in 3D. However, these reconstructions are limited to only 3D visualization without possible interactive exploration of geometric characteristics of cerebral structures. Refined understanding of the AVM angioarchitecture prior to treatment is mandatory and vascular segmentation is an important preliminary step that allow physicians analyze the complex vascular networks and can help guide microcatheters navigation and embolization of AVM. Methods: A deep learning method was developed for the segmentation of 3DRA images of AVM patients. The method uses a fully convolutional neural network with a U-Net-like architecture and a DenseNet backbone. A compound loss function, combining Cross Entropy and Focal Tversky, is employed for robust segmentation. Binary masks automatically generated from region-growing segmentation have been used to train and validate our model. Results: The developed network was able to achieve the segmentation of the vessels and the malformation and significantly outperformed the region-growing algorithm. Our experiments were performed on 9 AVM patients. The trained network achieved a Dice Similarity Coefficient (DSC) of 80.43%, surpassing other U-Net like architectures and the region-growing algorithm on the manually approved test set by physicians. Conclusions: This work demonstrates the potential of a learning-based segmentation method for characterizing very complex and tiny vascular structures even when the training phase is performed with the results of an automatic or a semi-automatic method. The proposed method can contribute to the planning and guidance of endovascular procedures.

BACKGROUND: Carotid atherosclerosis plaque rupture is an important cause of myocardial infarction and stroke. The effective segmentation of ultrasound images of carotid atherosclerotic plaques aids clinicians to accurately assess plaque stability. At present, this procedure relies mainly on the experience of the medical practitioner to manually segment the ultrasound image of the carotid atherosclerotic plaque. This method is also time-consuming. OBJECTIVE: This study intends to establish an automatic intelligent segmentation method of ultrasound images of carotid plaque. METHODS: The present study combined the U-Net and DenseNet networks, to automatically segment the ultrasound images of carotid atherosclerotic plaques. The same test set was selected and segmented using the traditional U-Net network and the ResUNet network. The prediction results of the three network models were compared using Dice (Dice similarity coefficient), and VOE (volumetric overlap error) coefficients. RESULTS: Compared with the existing U-Net network and ResUNet network, the Dense-UNet network exhibited an optimal effect on the automated segmentation of the ultrasound images. CONCLUSION: The Dense-UNet network could realize the automatic segmentation of atherosclerotic plaque ultrasound images, and it could assist medical practitioners in plaque evaluation. [ABSTRACT FROM AUTHOR]

Liver cancer is one of frequent causes of death from malignancy in the world. Owing to the outstanding advantages of computer-aided diagnosis and deep learning, fully automatic segmentation of computed tomography (CT) images turned into a research hotspot over the years. The liver has quite low contrast with the surrounding tissues, together with its lesion areas are thoroughly complex. To deal with these problems, we proposed effective methods for enhancing features and processed public datasets from Liver Tumor Segmentation Challenge (LITS) for the verification. In this experiment, data pre-processing based on the image enhancement and noise reduction. This study redesigned the original UNet with two novel modules and named it DResUNet which was applied deformable convolution. The first module aimed to recalibrate information by the channel and spatial dimension. The other module enriched deep information of these liver CT images through fusing multi-scale features. Besides, we used cross-entropy loss function for adaptive weights to solve the troubles of class imbalance in the dataset samples. These can improve the performance of the network in-depth and breadth feature learning to deal with many complex segmentation scenes in abdominal CT images. More importantly, the effect of predicted images fully proved that our methods are highly competitive among the segmentation of liver and liver tumors. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the repeatability of cartilage volume and thickness values at 1.5 T MRI using a fully automatic cartilage segmentation method and reproducibility of the method between 1.5 T and 3 T data., Methods: The study included 20 knee joints from 10 healthy subjects with each subject having undergone double-knee MRI. All knees were scanned at 1.5 T and 3 T MR scanners using a three-dimensional (3D) high-resolution dual-echo in steady state (DESS) sequence. Cartilage volume and thickness of 21 subregions were quantified using a fully automatic cartilage segmentation research application (MR Chondral Health, version 3.0, Siemens Healthcare, Erlangen, Germany). The volume and thickness values derived from fully automatically computed segmentation masks were analyzed for the scan-rescan data from the same volunteers. The accuracy of the automatic segmentation of the cartilage in 1.5 T images was evaluated by the dice similarity coefficient (DSC) and Hausdorff distance (HD) using the manually corrected segmentation as a reference. The volume and thickness values calculated from 1.5 T and 3 T were also compared., Results: No statistically significant differences were found for cartilage thickness or volume across all subregions between the scan-rescanned data at 1.5 T (P > 0.05). The mean DSC between the fully automatic and manually corrected knee cartilage segmentation contours at 1.5 T was 0.9946. The average value of HD was 2.41 mm. Overall, there was no statistically significant difference in the cartilage volume or thickness in most-subregions between the two field strengths (P > 0.05) except for the medial region of femur and tibia. Bland-Altman plot and intraclass correlation coefficient (ICC) showed high consistency between results obtained based on same and different scanning sequences., Conclusion: The cartilage segmentation software had high repeatability for DESS images obtained from the same device. In addition, the overall reproducibility of the images obtained from equipment of two different field strengths was satisfactory., (© 2023. The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB).)

Background: Computer-aided diagnosis (CAD) can aid radiologists in quantifying prostate cancer, and MRI segmentation plays an essential role in CAD applications. Clinical experience shows that prostate cancer occurs predominantly in the peripheral zone (PZ) and there exist different evaluation criteria for different regions in the Prostate Imaging Reporting and Data System (PI-RADS).Purpose: To develop a fully automatic approach to segmenting the prostate outer contour and the PZ contour with high efficacy.Population: In all, 163 subjects, including 61 healthy subjects and 102 prostate cancer patients. For each subject, all slices that contained prostate gland in diffusion-weighted images (DWIs) and T2 -weighted images (T2 WIs) were selected as the datasets.Field Strength: T2 -weighted, DWI at 3.0T.Assessment: The computer-generated segmentation results were compared with the manual outlining results that were depicted by two experts with more than 5 years' experience. Dice similarity coefficient (DSC), false-positive rate (FPR), and false-negative rate (FNR) were used to compared the algorithm and manual segmentation results.Statistical Tests: A paired t-test was adopted for comparison between our method and classical U-Net segmentation methods.Results: The mean DSC was 92.7 ± 4.2% for the total whole prostate gland and 79.3 ± 10.4% for the total peripheral zone. Compared with classical U-Net segmentation methods, our segmentation precision was significantly higher (P < 0.001).Data Conclusion: By cropping the region of interest and cascading the two networks, our method balances the positive and negative sample gradually, and results in higher segmentation accuracy. This fully automatic strategy could provide satisfactory performance in prostate DWIs and T2 WIs-based image segmentation.Level Of Evidence: 2 Technical Efficacy Stage 1 J. Magn. Reson. Imaging 2019;49:1149-1156. [ABSTRACT FROM AUTHOR]

Purpose: As part of a program to implement automatic lesion detection methods for whole body magnetic resonance imaging (MRI) in oncology, we have developed, evaluated, and compared three algorithms for fully automatic, multiorgan segmentation in healthy volunteers. Methods: The first algorithm is based on classification forests (CFs), the second is based on 3D convolutional neural networks (CNNs) and the third algorithm is based on a multi-atlas (MA) approach. We examined data from 51 healthy volunteers, scanned prospectively with a standardized, multiparametric whole body MRI protocol at 1.5 T. The study was approved by the local ethics committee and written consent was obtained from the participants. MRI data were used as input data to the algorithms, while training was based on manual annotation of the anatomies of interest by clinical MRI experts. Fivefold cross-validation experiments were run on 34 artifact-free subjects. We report three overlap and three surface distance metrics to evaluate the agreement between the automatic and manual segmentations, namely the dice similarity coefficient (DSC), recall (RE), precision (PR), average surface distance (ASD), root-mean-square surface distance (RMSSD), and Hausdorff distance (HD). Analysis of variances was used to compare pooled label metrics between the three algorithms and the DSC on a 'per-organ' basis. A Mann-Whitney U test was used to compare the pooled metrics between CFs and CNNs and the DSC on a 'per-organ' basis, when using different imaging combinations as input for training. Results: All three algorithms resulted in robust segmenters that were effectively trained using a relatively small number of datasets, an important consideration in the clinical setting. Mean overlap metrics for all the segmented structures were: CFs: DSC = 0.70 ± 0.18, RE = 0.73 ± 0.18, PR = 0.71 ± 0.14, CNNs: DSC = 0.81 ± 0.13, RE = 0.83 ± 0.14, PR = 0.82 ± 0.10, MA: DSC = 0.71 ± 0.22, RE = 0.70 ± 0.34, PR = 0.77 ± 0.15. Mean surface distance metrics for all the segmented structures were: CFs: ASD = 13.5 ± 11.3 mm, RMSSD = 34.6 ± 37.6 mm and HD = 185.7 ± 194.0 mm, CNNs; ASD = 5.48 ± 4.84 mm, RMSSD = 17.0 ± 13.3 mm and HD = 199.0 ± 101.2 mm, MA: ASD = 4.22 ± 2.42 mm, RMSSD = 6.13 ± 2.55 mm, and HD = 38.9 ± 28.9 mm. The pooled performance of CFs improved when all imaging combinations (T2w + T1w + DWI) were used as input, while the performance of CNNs deteriorated, but in neither case, significantly. CNNs with T2w images as input, performed significantly better than CFs with all imaging combinations as input for all anatomical labels, except for the bladder. Conclusions: Three state-of-the-art algorithms were developed and used to automatically segment major organs and bones in whole body MRI; good agreement to manual segmentations performed by clinical MRI experts was observed. CNNs perform favorably, when using T2w volumes as input. Using multimodal MRI data as input to CNNs did not improve the segmentation performance. [ABSTRACT FROM AUTHOR]

Prostate segmentation from transrectal ultrasound (TRUS) images plays an important role in the diagnosis and treatment planning of prostate cancer. In this paper, a fully automatic slice-based segmentation method was developed to segment TRUS prostate images. The initial prostate contour was determined using a novel method based on the radial bas-relief (RBR) method, and a false edge removal algorithm proposed here in. 2D slice-based propagation was used in which the contour on each image slice was deformed using a level-set evolution model, which was driven by edge-based and region-based energy fields generated by dyadic wavelet transform. The optimized contour on an image slice propagated to the adjacent slice, and subsequently deformed using the level-set model. The propagation continued until all image slices were segmented. To determine the initial slice where the propagation began, the initial prostate contour was deformed individually on each transverse image. A method was developed to self-assess the accuracy of the deformed contour based on the average image intensity inside and outside of the contour. The transverse image on which highest accuracy was attained was chosen to be the initial slice for the propagation process. Evaluation was performed for 336 transverse images from 15 prostates that include images acquired at mid-gland, base and apex regions of the prostates. The average mean absolute difference (MAD) between algorithm and manual segmentations was 0.79±0.26mm, which is comparable to results produced by previously published semi-automatic segmentation methods. Statistical evaluation shows that accurate segmentation was not only obtained at the mid-gland, but also at the base and apex regions., (Copyright © 2016 Elsevier Ltd. All rights reserved.)