ArticleArXiv2025
Modality-AGnostic Image Cascade (MAGIC) for Multi-Modality Cardiac Substructure Segmentation.
Article in ArXiv, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Purpose: Cardiac substructures are essential in thoracic radiation therapy planning to minimize risk of radiation-induced heart disease. Deep learning (DL) offers efficient methods to reduce contouring burden but lacks generalizability across different modalities and overlapping structures. This work introduces and validates a Modality-AGnostic Image Cascade (MAGIC) for comprehensive and multi-modal cardiac substructure segmentation. Materials and methods: MAGIC is implemented through replicated encoding and decoding branches of an nnU-Net-based, U-shaped backbone conserving the function of a single model. Twenty cardiac substructures (heart, chambers, great vessels (GVs), valves, coronary arteries (CAs), and conduction nodes) from simulation CT (Sim-CT), low-field MR-Linac, and cardiac CT angiography (CCTA) modalities were manually delineated and used to train (n=76), validate (n=15), and test (n=30) MAGIC. Twelve comparison models (four segmentation subgroups across three modalities) were equivalently trained. All methods were compared for training efficiency and against reference contours using the Dice Similarity Coefficient (DSC) and two-tailed Wilcoxon Signed-Rank test (threshold, p<0.05). Results: Average DSC scores were 0.75±0.16 (Heart, 0.96±0.01; Chambers, 0.89±0.05; GVs, 0.81±0.09; CAs, 0.60±0.13; Valves, 0.70±0.18; Nodes, 0.66±0.12) for Sim-CT, 0.68±0.21 (Heart, 0.94±0.01; Chambers, 0.87±0.05; GVs, 0.72±0.18; CAs, 0.50±0.18; Valves, 0.62±0.16; Nodes, 0.52±0.16) for MR-Linac, and 0.80±0.16 (Heart, 0.95±0.01; Chambers, 0.93±0.04; GVs, 0.84±0.06; CAs, 0.77±0.12; Valves, 0.68±0.23; Nodes, 0.72±0.11) for CCTA. MAGIC outperforms the comparison in 57% of cases, with limited statistical differences. Conclusions: MAGIC offers an effective and accurate segmentation solution that is lightweight and capable of segmenting multiple modalities and overlapping structures in a single model. MAGIC further enables clinical implementation by simplifying the computational requirements and offering unparalleled flexibility for clinical settings.
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40980770PMC12447710What OpenQuestion holds
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