Ultrasound prostate segmentation based on multidirectional deeply supervised V‐Net

Lei, Yang; Tian, Sibo; He, Xiuxiu; Wang, Tonghe; Wang, Bo; Patel, Pretesh; Jani, Ashesh B.; Mao, Hui; Curran, Walter J.; Liu, Tian; Yang, Xiaofeng

doi:10.1002/mp.13577

Cited by 107 publications

(96 citation statements)

References 38 publications

(56 reference statements)

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“…We calculated the dice similarity coefficient (DSC), precision score, recall score, Hausdorff distance (HD), mean surface distance (MSD), and the residual mean square distance (RMSD) to evaluate the accuracy of our segmentation method. The calculation of these metrics is introduced in recent studies . The DSC, precision, and recall scores are used to quantify volume similarity between two contours.…”

Section: Methodsmentioning

confidence: 99%

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CT prostate segmentation based on synthetic MRI‐aided deep attention fully convolution network

et al. 2019

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Purpose Accurate segmentation of the prostate on computed tomography (CT) for treatment planning is challenging due to CT's poor soft tissue contrast. Magnetic resonance imaging (MRI) has been used to aid prostate delineation, but its final accuracy is limited by MRI‐CT registration errors. We developed a deep attention‐based segmentation strategy on CT‐based synthetic MRI (sMRI) to deal with the CT prostate delineation challenge without MRI acquisition. Methods and materials We developed a prostate segmentation strategy which employs an sMRI‐aided deep attention network to accurately segment the prostate on CT. Our method consists of three major steps. First, a cycle generative adversarial network was used to estimate an sMRI from CT images. Second, a deep attention fully convolution network was trained based on sMRI and the prostate contours deformed from MRIs. Attention models were introduced to pay more attention to prostate boundary. The prostate contour for a query patient was obtained by feeding the patient's CT images into the trained sMRI generation model and segmentation model. Results The segmentation technique was validated with a clinical study of 49 patients by leave‐one‐out experiments and validated with an additional 50 patients by hold‐out test. The Dice similarity coefficient, Hausdorff distance, and mean surface distance indices between our segmented and deformed MRI‐defined prostate manual contours were 0.92 ± 0.09, 4.38 ± 4.66, and 0.62 ± 0.89 mm, respectively, with leave‐one‐out experiments, and were 0.91 ± 0.07, 4.57 ± 3.03, and 0.62 ± 0.65 mm, respectively, with hold‐out test. Conclusions We have proposed a novel CT‐only prostate segmentation strategy using CT‐based sMRI, and validated its accuracy against the prostate contours that were manually drawn on MRI images and deformed to CT images. This technique could provide accurate prostate volume for treatment planning without requiring MRI acquisition, greatly facilitating the routine clinical workflow.

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Section: Methodsmentioning

confidence: 99%

“…The calculation of these metrics is introduced in recent studies. 25,[32][33][34] The DSC, precision, and recall scores are used to quantify volume similarity between two contours. The HD, MSD, and RMSD metrics are used to quantify boundary similarity between two surfaces.…”

Section: F Quantitative Measurementsmentioning

confidence: 99%

CT prostate segmentation based on synthetic MRI‐aided deep attention fully convolution network

et al. 2019

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“…The field of medical image registration has been evolving rapidly with hundreds of papers published each year. Recently, DL-based methods have changed the landscape of medical image processing research and achieved the-state-of-art performances in many applications [25,27,45,58,84,85,86,88,89,97,98,156,157,158,160,161]. However, deep learning in medical image registration has not been extensively studied until the past three to four years.…”

Section: Introductionmentioning

confidence: 99%

Deep learning in medical image registration: a review

et al. 2020

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This paper presents a review of deep learning (DL) based medical image registration methods. We summarized the latest developments and applications of DL-based registration methods in the medical field. These methods were classified into seven categories according to their methods, functions and popularity. A detailed review of each category was presented, highlighting important contributions and identifying specific challenges. A short assessment was presented following the detailed review of each category to summarize its achievements and future potentials. We provided a comprehensive comparison among DL-based methods for lung and brain registration using benchmark datasets. Lastly, we analyzed the statistics of all the cited works from various aspects, revealing the popularity and future trend of DL-based medical image registration. 1 arXiv:1912.12318v1 [eess.IV] 27 Dec 2019 1. Summarize the latest developments in DL-based medical image registration. 2. Highlight contributions, identify challenges and outline future trends. 3. Provide detailed statistics on recent publications from different perspectives. 2 Deep Learning 2 Convolutional Neural NetworkConvolutional neural network (CNN) is a class of deep neural networks with regularized multilayer perceptron. CNN uses convolution operation in place of general matrix multiplication in simple neural networks. The convolutional filters and operations in CNN make it suitable for visual imagery signal processing. Because of its excellent feature extraction ability, CNN is one of the most successful models for image analysis. Since the breakthrough of AlexNet [79], many variants of CNN have been proposed and have achieved the-state-of-art performances in various image processing tasks. A typical CNN usually consists of multiple convolutional layers, max pooling layers, batch normalization layers, dropout layers, a sigmoid or softmax layer. In each convolutional layer, multiple channels of feature maps were extracted by sliding trainable convolutional kernels across the input feature maps. Hierarchical features with high-level abstraction are extracted using multiple convolutional layers. These feature maps usually go through multiple fully connected layer before reaching the final decision layer. Max pooling layers are often used to reduce the image sizes and to promote spatial invariance of the network. Batch normalization is used to reduce internal covariate shift among the training samples. Weight regularization and dropout layers are used to alleviate data overfitting. The loss function is defined as the difference between the predicted and the target output. CNN is usually trained by minimizing the loss via gradient back propagation using optimization methods. Many different types of network architectures have been proposed to improve the performance of CNN [93]. U-Net proposed by Ronneberger et al. is among one of the most used network architectures [120]. U-Net was originally used to perform neuronal structures segmentation. U-Net adopts symmetrical contractiv...

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“…The V‐Net is another popular FCN variant that has a similar structure to the 3D U‐Net, but there are some key differences: the V‐Net uses 5 × 5 × 5 convolution kernels instead of 3 × 3 × 3 in U‐Net; the amount of convolution layers in each tier range from 1 to 3 rather than being constantly 2 in U‐Net; and downsampling is performed by 2‐stride convolution rather than max‐pooling to make gradient calculation easier. Most importantly, skip connections are used to reintroduce high level features that were learned earlier to aid loss convergence.…”

Section: Methodsmentioning

confidence: 99%

A 3D deep convolutional neural network approach for the automated measurement of cerebellum tracer uptake in FDG PET‐CT scans

et al. 2020

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Purpose The purpose of this work was to assess the potential of deep convolutional neural networks in automated measurement of cerebellum tracer uptake in F‐18 fluorodeoxyglucose (FDG) positron emission tomography (PET) scans. Methods Three different three‐dimensional (3D) convolutional neural network architectures (U‐Net, V‐Net, and modified U‐Net) were implemented and compared regarding their performance in 3D cerebellum segmentation in FDG PET scans. For network training and testing, 134 PET scans with corresponding manual volumetric segmentations were utilized. For segmentation performance assessment, a fivefold cross‐validation was used, and the Dice coefficient as well as signed and unsigned distance errors were calculated. In addition, standardized uptake value (SUV) uptake measurement performance was assessed by means of a statistical comparison to an independent reference standard. Furthermore, a comparison to a previously reported active‐shape‐model‐based approach was performed. Results Out of the three convolutional neural networks investigated, the modified U‐Net showed significantly better segmentation performance. It achieved a Dice coefficient of 0.911 ± 0.026, a signed distance error of 0.220 ± 0.103 mm, and an unsigned distance error of 1.048 ± 0.340 mm. When compared to the independent reference standard, SUV uptake measurements produced with the modified U‐Net showed no significant error in slope and intercept. The estimated reduction in total SUV measurement error was 95.1%. Conclusions The presented work demonstrates the potential of deep convolutional neural networks in automated SUV measurement of reference regions. While it focuses on the cerebellum, utilized methods can be generalized to other reference regions like the liver or aortic arch. Future work will focus on combining lesion and reference region analysis into one approach.

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Ultrasound prostate segmentation based on multidirectional deeply supervised V‐Net

Cited by 107 publications

References 38 publications

CT prostate segmentation based on synthetic MRI‐aided deep attention fully convolution network

CT prostate segmentation based on synthetic MRI‐aided deep attention fully convolution network

Deep learning in medical image registration: a review

A 3D deep convolutional neural network approach for the automated measurement of cerebellum tracer uptake in FDG PET‐CT scans

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