Modality-AGnostic image Cascade (MAGIC) for multi-modality cardiac substructure segmentation.

ABSTRACT:

Purpose

Cardiac substructure delineation is emerging in treatment planning to minimize the risk of radiation-induced heart disease. Deep learning offers efficient methods to reduce contouring burden but currently lacks generalizability across different modalities and overlapping structures. This work introduces and validates a Modality-AGnostic Image Cascade (MAGIC) deep-learning pipeline for comprehensive and multi-modal cardiac substructure segmentation.

Materials and methods

MAGIC is implemented through replicated encoding and decoding branches of an nnU-Net backbone to handle multi-modality inputs and overlapping labels. First benchmarked on the multi-modality whole-heart segmentation (MMWHS) dataset including cardiac CT-angiography (CCTA) and MR modalities, twenty cardi

SUBMITTER: Summerfield N 

PROVIDER: S-EPMC12931389 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature