Impact of annotation imperfections and auto-curation for deep learning-based organ-at-risk segmentation.

ABSTRACT:

Background and purpose

Segmentation imperfections (noise) in radiotherapy organ-at-risk segmentation naturally arise from specialist experience and image quality. Using clinical contours can result in sub-optimal convolutional neural network (CNN) training and performance, but manual curation is costly. We address the impact of simulated and clinical segmentation noise on CNN parotid gland (PG) segmentation performance and provide proof-of-concept for an easily implemented auto-curation countermeasure.

Methods and materials

The impact of segmentation imperfections was investigated by simulating noise in clean, high-quality segmentations. Curation efficacy was tested by removing lowest-scoring Dice similarity coefficient (DSC) cases early during CNN training, both in simulate

SUBMITTER: Strijbis VIJ 

PROVIDER: S-EPMC11667007 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature