Meeting Banner
Abstract #2849

An automatic prostate gland and peripheral zone segmentations method based on cascaded fully convolution network

Yi Zhu1, Rong Wei1, Lian Ding1, Ge Gao2, Xiaodong Zhang2, Xiaoying Wang1,2, Jue Zhang1,3, and Jing Fang1,3

1Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China, 2Department of Radiology, Peking University First Hospital, Beijing, China, 3College of Engineering, Peking University, Beijing, China

Automatic segmentation both in the whole prostate gland and the peripheral zone is a meaningful work, because there are different evaluation criteria for different regions according to prostate imaging reporting and data system's advice. Here we show a new method base on deep learning which can get the prostate outer contour and the peripheral zone contour fast and accurately without any manual intervention. The mean segmentation accuracies for 262 images are 94.87% ( the whole prostate gland) and 85.66% (the peripheral zone). Even in some extreme cases, such as hyperplasia and cancer, our method shows relatively good performance.

This abstract and the presentation materials are available to members only; a login is required.

Join Here