MRAnnotator: multi-anatomy and many-sequence MRI segmentation of 44 structures.

ABSTRACT:

Purpose

To develop a deep learning model for multi-anatomy segmentation of diverse anatomic structures on MRI.

Materials and methods

In this retrospective study, 44 structures were annotated using a model-assisted workflow with manual human finalization in 2 curated datasets: an internal dataset of 1518 MRI sequences (843 patients) from various clinical sites within a health system, and an external dataset of 397 MRI sequences (263 patients) from an independent imaging center for benchmarking. The internal dataset was used to train an nnU-Net model (MRAnnotator), while the external dataset evaluated MRAnnotator's generalizability across significant image acquisition distribution shifts. MRAnnotator was further benchmarked against an nnU-Net model trained on the AMOS dataset

SUBMITTER: Zhou A 

PROVIDER: S-EPMC12429175 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature