A combinatorial Bayesian and Dirichlet model for prostate MR image segmentation using probabilistic image features

Li, Ang; Li, Changyang; Wang, Xiuying; Eberl, Stefan; Feng, Dagan; Fulham, Michael

doi:10.1088/0031-9155/61/16/6085

Cited by 4 publications

(1 citation statement)

References 43 publications

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“…Prostate cancer is the leading cancer diagnosis among American men . Magnetic resonance (MR) imaging is a noninvasive modality that is increasingly recognized as the gold standard over computed tomography (CT) for prostate imaging due to its superior soft tissue contrast . Accurate prostate delineation is crucial for treatment planning and effective image‐guided radiotherapy .…”

Section: Introductionmentioning

confidence: 99%

Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation

et al. 2019

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Purpose: Reliable automated segmentation of the prostate is indispensable for image-guided prostate interventions. However, the segmentation task is challenging due to inhomogeneous intensity distributions, variation in prostate anatomy, among other problems. Manual segmentation can be time-consuming and is subject to inter-and intraobserver variation. We developed an automated deep learning-based method to address this technical challenge. Methods: We propose a three-dimensional (3D) fully convolutional networks (FCN) with deep supervision and group dilated convolution to segment the prostate on magnetic resonance imaging (MRI). In this method, a deeply supervised mechanism was introduced into a 3D FCN to effectively alleviate the common exploding or vanishing gradients problems in training deep models, which forces the update process of the hidden layer filters to favor highly discriminative features. A group dilated convolution which aggregates multiscale contextual information for dense prediction was proposed to enlarge the effective receptive field of convolutional neural networks, which improve the prediction accuracy of prostate boundary. In addition, we introduced a combined loss function including cosine and cross entropy, which measures similarity and dissimilarity between segmented and manual contours, to further improve the segmentation accuracy. Prostate volumes manually segmented by experienced physicians were used as a gold standard against which our segmentation accuracy was measured. Results: The proposed method was evaluated on an internal dataset comprising 40 T2-weighted prostate MR volumes. Our method achieved a Dice similarity coefficient (DSC) of 0.86 AE 0.04, a mean surface distance (MSD) of 1.79 AE 0.46 mm, 95% Hausdorff distance (95%HD) of 7.98 AE 2.91 mm, and absolute relative volume difference (aRVD) of 15.65 AE 10.82. A public dataset (PROMISE12) including 50 T2-weighted prostate MR volumes was also employed to evaluate our approach. Our method yielded a DSC of 0.88 AE 0.05, MSD of 1.02 AE 0.35 mm, 95% HD of 9.50 AE 5.11 mm, and aRVD of 8.93 AE 7.56. Conclusion: We developed a novel deeply supervised deep learning-based approach with a group dilated convolution to automatically segment the MRI prostate, demonstrated its clinical feasibility, and validated its accuracy against manual segmentation. The proposed technique could be a useful tool for image-guided interventions in prostate cancer.

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Section: Introductionmentioning

confidence: 99%

Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation

et al. 2019

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PRF-RW: a progressive random forest-based random walk approach for interactive semi-automated pulmonary lobes segmentation

Chen

et al. 2020

Int. J. Mach. Learn. & Cyber.

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Multi-view collaborative segmentation for prostate MRI images

Wang

Tang

Cui

et al. 2017

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Prostate delineation from MRI images is a prolonged challenging issue partially due to appearance variations across patients and disease progression. To address these challenges, our proposed collaborative method takes into account the computed multiple label-relevance maps as multiple views for learning the optimal boundary delineation. In our method, we firstly extracted multiple label-relevance maps to represent the affinities between each unlabeled pixel to the pre-defined labels to avoid the selection of handcrafted features. Then these maps were incorporated in a collaborative clustering to learn the adaptive weights for an optimal segmentation which overcomes the seeds selection sensitivity problems. The segmentation results were evaluated over 22 prostate MRI patient studies with respect to dice similarity coefficient (DSC), absolute relative volume difference (ARVD) and average symmetric surface distance (ASSD) (mm). The results and t-Test demonstrated that the proposed method improved the segmentation accuracy and robustness and the improvement was statistically significant.

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A combinatorial Bayesian and Dirichlet model for prostate MR image segmentation using probabilistic image features

Cited by 4 publications

References 43 publications

Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation

Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation

PRF-RW: a progressive random forest-based random walk approach for interactive semi-automated pulmonary lobes segmentation

Multi-view collaborative segmentation for prostate MRI images

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