LGESynthNet: Controlled Scar Synthesis for Improved Scar Segmentation in Cardiac LGE-MRI Imaging

• URL: http://arxiv.org/abs/2603.18356v1
• Date: Wed, 18 Mar 2026 23:40:33 GMT
• Title: LGESynthNet: Controlled Scar Synthesis for Improved Scar Segmentation in Cardiac LGE-MRI Imaging
• Abstract summary: We introduce LGE SynthNet, a latent diffusion-based framework for controllable enhancement synthesis.<n>The model integrates: (a) a reward model for conditioning-specific supervision, (b) a captioning module for descriptive text prompts, and (c) a biomedical text encoder.<n> trained on just 429 images (79 patients), it produces anatomically coherent samples.
• Score: 21.180056139848528
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Segmentation of enhancement in LGE cardiac MRI is critical for diagnosing various ischemic and non-ischemic cardiomyopathies. However, creating pixel-level annotations for these images is challenging and labor-intensive, leading to limited availability of annotated data. Generative models, particularly diffusion models, offer promise for synthetic data generation, yet many rely on large training datasets and often struggle with fine-grained conditioning control, especially for small or localized features. We introduce LGESynthNet, a latent diffusion-based framework for controllable enhancement synthesis, enabling explicit control over size, location, and transmural extent. Formulated as inpainting using a ControlNet-based architecture, the model integrates: (a) a reward model for conditioning-specific supervision, (b) a captioning module for anatomically descriptive text prompts, and (c) a biomedical text encoder. Trained on just 429 images (79 patients), it produces realistic, anatomically coherent samples. A quality control filter selects outputs with high conditioning-fidelity, which when used for training augmentation, improve downstream segmentation and detection performance, by up-to 6 and 20 points respectively.

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