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LISynSeg: Data-Centric Label-to-Image Synthesis for Cross-Modality Whole-Heart Segmentation

arXiv · AI, language, vision and robotics · article · Aug 31, 2026 · UTC

Whole-heart segmentation (WHS) in computed tomography (CT) and magnetic resonance imaging (MRI) is affected by acquisition shifts and heterogeneous cardiac annotations. Existing WHS systems combine architectural design, transfer learning, and generic spatial or intensity augmentation. We investigate whether changes to data augmentation and training supervision can improve cross-modality WHS while the segmentation architecture is held constant. We present LISynSeg, a data-centric approach that augments real-image nnU-Net training with label-to-image synthesis. Synthetic volumes are generated fr

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Evidence & attribution

arXiv · AI, language, vision and robotics · 2026-08-31T16:47:21.000Z

First collected: 2026-09-21T06:41:57.136Z. This is not the publication date.