Label-set impact on deep learning-based prostate segmentation on MRI.

ABSTRACT:

Background

Prostate segmentation is an essential step in computer-aided detection and diagnosis systems for prostate cancer. Deep learning (DL)-based methods provide good performance for prostate gland and zones segmentation, but little is known about the impact of manual segmentation (that is, label) selection on their performance. In this work, we investigated these effects by obtaining two different expert label-sets for the PROSTATEx I challenge training dataset (n = 198) and using them, in addition to an in-house dataset (n = 233), to assess the effect on segmentation performance. The automatic segmentation method we used was nnU-Net.

Results

The selection of training/testing label-set had a significant (p < 0.001) impact on model performance. Furthermore, it was found

SUBMITTER: Meglic J 

PROVIDER: S-EPMC10519913 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature