A Quality Control System for Automated Prostate Segmentation on T2-Weighted MRI.

ABSTRACT: Computer-aided detection and diagnosis (CAD) systems have the potential to improve robustness and efficiency compared to traditional radiological reading of magnetic resonance imaging (MRI). Fully automated segmentation of the prostate is a crucial step of CAD for prostate cancer, but visual inspection is still required to detect poorly segmented cases. The aim of this work was therefore to establish a fully automated quality control (QC) system for prostate segmentation based on T2-weighted MRI. Four different deep learning-based segmentation methods were used to segment the prostate for 585 patients. First order, shape and textural radiomics features were extracted from the segmented prostate masks. A reference quality score (QS) was calculated for each automated segmentation in comparis

SUBMITTER: Sunoqrot MRS 

PROVIDER: S-EPMC7555425 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature