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Fully automated prostate whole gland and central gland segmentation on MRI using holistically nested networks with short connections
Authors
Ruida Cheng
Peter Choyke
+10 more
William Gandler
Dakai Jin
Nathan Lay
Matthew J. Mcauliffe
Evan S. Mccreedy
Peter Pinto
Tom Pohida
Holger R. Roth
Ronald M. Summers
Baris Turkbey
Publication date
1 April 2019
Publisher
Health Sciences Research Commons
Abstract
© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE). 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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George Washington University: Health Sciences Research Commons (HSRC)
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Last time updated on 03/12/2020