Harnessing clinical annotations to improve deep learning performance in prostate segmentation.

ABSTRACT:

Purpose

Developing large-scale datasets with research-quality annotations is challenging due to the high cost of refining clinically generated markup into high precision annotations. We evaluated the direct use of a large dataset with only clinically generated annotations in development of high-performance segmentation models for small research-quality challenge datasets.

Materials and methods

We used a large retrospective dataset from our institution comprised of 1,620 clinically generated segmentations, and two challenge datasets (PROMISE12: 50 patients, ProstateX-2: 99 patients). We trained a 3D U-Net convolutional neural network (CNN) segmentation model using our entire dataset, and used that model as a template to train models on the challenge datasets. We also trained

SUBMITTER: Sarma KV 

PROVIDER: S-EPMC8232529 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature