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

arXiv:2603.18356v1 Announce Type: new 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 ima

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Athira J. Jacob, Puneet Sharma, Daniel Rueckert · · 1 min read · 6 views
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arXiv:2603.18356v1 Announce Type: new 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.

Executive Summary

LGESynthNet presents a novel latent diffusion-based framework designed to address the challenges of annotating LGE cardiac MRI images by generating synthetic, controllable scar data. Given the scarcity and labor-intensive nature of annotated datasets, this model introduces a controlled synthesis mechanism via inpainting architecture, enabling precise control over scar size, location, and transmural extent. Trained on a modest dataset (429 images), LGESynthNet leverages a reward model, captioning module, and biomedical text encoder to produce anatomically coherent synthetic samples. The quality control filter enhances conditioning fidelity, translating into measurable improvements in downstream segmentation and detection metrics—up to 6 and 20 points respectively. This innovation addresses critical bottlenecks in medical imaging annotation and offers a scalable solution for synthetic data generation.

Key Points

  • ▸ LGESynthNet introduces a controllable latent diffusion framework for synthetic scar synthesis in LGE-MRI.
  • ▸ The model integrates a reward system, captioning module, and biomedical text encoder for anatomical precision.
  • ▸ Trained on a small dataset, it achieves significant improvements in segmentation performance via augmentation.

Merits

Strength in Controllability

The ability to explicitly control size, location, and transmural extent via a ControlNet-based inpainting architecture is a major innovation, enabling targeted synthesis without reliance on massive datasets.

Efficiency in Training

Achieving meaningful augmentation effects with only 429 images demonstrates the efficiency and effectiveness of the model’s conditioning mechanisms.

Demerits

Dataset Limitation

The training set of 429 images, while sufficient for validation, may limit generalizability across diverse clinical populations or scanner types, raising concerns about broader applicability.

Conditional Complexity

Fine-grained conditioning control, though effective, may introduce computational overhead or require careful tuning in clinical deployment.

Expert Commentary

LGESynthNet represents a pivotal advancement in the application of generative AI to cardiac imaging, particularly in overcoming the persistent hurdle of scarce annotated data. The integration of a ControlNet architecture with reward modeling and captioning is a sophisticated yet practical mechanism that aligns with the clinical reality of limited resources. Unlike prior diffusion-based models that rely on large-scale training data to achieve fine-grained control, LGESynthNet achieves comparable precision with a minimal dataset—a feat that challenges conventional assumptions about data volume requirements. Moreover, the quality control filter introduces a critical layer of accountability, mitigating risks associated with hallucinated or anatomically incoherent synthetic samples. While the dataset size remains a legitimate concern, the results speak to the potency of targeted conditioning rather than sheer quantity. This model may serve as a blueprint for similar applications in other imaging modalities and pathologies, potentially catalyzing a shift toward more efficient, data-efficient AI pipelines in medical diagnostics. The implications extend beyond cardiac MRI to any field where data annotation is a barrier to AI adoption.

Recommendations

  • ✓ Researchers should validate LGESynthNet across diverse scanner platforms and patient cohorts to assess generalizability.
  • ✓ Clinicians deploying this framework should incorporate the quality control filter into their validation workflows to ensure reliability.
  • ✓ Funding agencies and regulatory bodies should consider supporting replication studies to expand the model’s applicability and assess scalability.

Sources

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