The field of medical imaging segmentation is witnessing significant advancements with the integration of innovative techniques such as diffusion-based data augmentation, momentum equation-based regularization, and generative segmentation. These approaches aim to improve the accuracy and robustness of segmentation models, particularly in scenarios with scarce annotated data or complex anatomical structures. Notably, the incorporation of domain-specific priors and pathology-informed domain randomization strategies is enhancing the generalization capabilities of models, allowing for more effective analysis of rare but clinically significant malformations.

Some noteworthy papers in this regard include: Diffusion-Based Data Augmentation for Medical Image Segmentation, which proposes a novel framework for synthesizing abnormalities via inpainting on normal images. GS: Generative Segmentation via Label Diffusion, which formulates segmentation as a generative task via label diffusion, enabling end-to-end training with explicit control over spatial and semantic fidelity. Enhancing Corpus Callosum Segmentation in Fetal MRI via Pathology-Informed Domain Randomization, which demonstrates the effectiveness of incorporating prior knowledge of corpus callosum dysgenesis manifestations into a synthetic data generation pipeline.