We propose a 3D prostate segmentation method for transrectal ultrasound (TRUS) images, which is based on patch-based feature learning framework.
Mar 21, 2016 · We propose a 3D prostate segmentation method for transrectal ultrasound (TRUS) images, which is based on patch-based feature learning framework.
PDF | We propose a 3D prostate segmentation method for transrectal ultrasound (TRUS) images, which is based on patch-based feature learning framework.
A new prostate segmentation approach based on the optimal feature learning framework is developed, demonstrated its clinical feasibility, and validated its ...
3D Transrectal Ultrasound (TRUS) Prostate Segmentation Based on ...
www.ncbi.nlm.nih.gov › PMC6715140
Mar 21, 2016 · We propose a 3D prostate segmentation method for transrectal ultrasound (TRUS) images, which is based on patch-based feature learning ...
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Mar 3, 2024 · Liu, “3D transrectal ultrasound (TRUS) prostate segmentation based on optimal feature learning framework,” in Medical Imaging 2016: Image ...
A 3D segmentation method based on longitudinal image registration and machine learning is developed for transrectal ultrasound (TRUS) images, ...
Jun 29, 2015 · We have developed a new prostate segmentation approach based on the optimal feature learning framework, demonstrated its clinical ...
PDF | We developed a three-dimensional (3D) segmentation method for transrectal ultrasound (TRUS) images, which is based on longitudinal image.
May 10, 2019 · We developed a novel deeply supervised deep learning-based approach with reliable contour refinement to automatically segment the TRUS prostate, ...