Training Convolutional Networks for Prostate Segmentation With Limited Data.

ABSTRACT: Multi-zonal segmentation is a critical component of computer-aided diagnostic systems for detecting and staging prostate cancer. Previously, convolutional neural networks such as the U-Net have been used to produce fully automatic multi-zonal prostate segmentation on magnetic resonance images (MRIs) with performance comparable to human experts, but these often require large amounts of manually segmented training data to produce acceptable results. For institutions that have limited amounts of labeled MRI exams, it is not clear how much data is needed to train a segmentation model, and which training strategy should be used to maximize the value of the available data. This work compares how the strategies of transfer learning and aggregated training using publicly available external data ca

SUBMITTER: Saunders SL 

PROVIDER: S-EPMC8438764 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature