Fully automated prostate whole gland and central gland segmentation on MRI using holistically nested networks with short connections

Ruida Cheng; Nathan S. Lay; Holger R. Roth; Baris Turkbey; Dakai Jin; William Gandler; Evan S. McCreedy; Tom J. Pohida; Peter A. Pinto; Peter L. Choyke; Matthew J. McAuliffe; Ronald M. Summers

doi:10.1117/1.JMI.6.2.024007

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5 June 2019 Fully automated prostate whole gland and central gland segmentation on MRI using holistically nested networks with short connections

Ruida Cheng, Nathan S. Lay, Holger R. Roth, Baris Turkbey, Dakai Jin, William Gandler, Evan S. McCreedy, Tom J. Pohida, Peter A. Pinto, Peter L. Choyke, Matthew J. McAuliffe, Ronald M. Summers

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J. of Medical Imaging, 6(2), 024007 (2019). https://doi.org/10.1117/1.JMI.6.2.024007

Abstract

Accurate and automated prostate whole gland and central gland segmentations on MR images are essential for aiding any prostate cancer diagnosis system. Our work presents a 2-D orthogonal deep learning method to automatically segment the whole prostate and central gland from T2-weighted axial-only MR images. The proposed method can generate high-density 3-D surfaces from low-resolution (z axis) MR images. In the past, most methods have focused on axial images alone, e.g., 2-D based segmentation of the prostate from each 2-D slice. Those methods suffer the problems of over-segmenting or under-segmenting the prostate at apex and base, which adds a major contribution for errors. The proposed method leverages the orthogonal context to effectively reduce the apex and base segmentation ambiguities. It also overcomes jittering or stair-step surface artifacts when constructing a 3-D surface from 2-D segmentation or direct 3-D segmentation approaches, such as 3-D U-Net. The experimental results demonstrate that the proposed method achieves 92.4 % ± 3 % Dice similarity coefficient (DSC) for prostate and DSC of 90.1 % ± 4.6 % for central gland without trimming any ending contours at apex and base. The experiments illustrate the feasibility and robustness of the 2-D-based holistically nested networks with short connections method for MR prostate and central gland segmentation. The proposed method achieves segmentation results on par with the current literature.

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Ruida Cheng, Nathan S. Lay, Holger R. Roth, Baris Turkbey, Dakai Jin, William Gandler, Evan S. McCreedy, Tom J. Pohida, Peter A. Pinto, Peter L. Choyke, Matthew J. McAuliffe, and Ronald M. Summers "Fully automated prostate whole gland and central gland segmentation on MRI using holistically nested networks with short connections," Journal of Medical Imaging 6(2), 024007 (5 June 2019). https://doi.org/10.1117/1.JMI.6.2.024007

Received: 30 October 2018; Accepted: 15 May 2019; Published: 5 June 2019

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