How much data do you need? An analysis of pelvic multi-organ segmentation in a limited data context.

ABSTRACT: Training deep learning models generally requires large, costly datasets which can limit their application towards in-house segmentation tasks. This study investigates the trade-off in dataset size within the context of pelvic multi-organ MR segmentation where we evaluate the performance of nnU-Net, a well-known segmentation model, under conditions of limited domain and data availability. 12 participants undergoing treatment on an Elekta Unity were recruited, acquiring 58 MR images, with 4 participants (12 images) withheld for testing. Prostate, seminal vesicles (SV), bladder and rectum were contoured in each image by a radiation oncologist. Seven models were trained on progressively smaller subsets of the training dataset, simulating a limited dataset setting. To investigate the efficacy o

SUBMITTER: Lunardo F 

PROVIDER: S-EPMC11996946 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature