Andrew Yu

Medical image segmentation methods face the challenge of domain transfer, where performance degrades due to distribution shifts between source and target domains. This paper adapts SAM2, a general-purpose video segmentation model, for zero-shot single-prompt 3D knee MRI segmentation by treating volumetric slices as individual video frames and leveraging SAM2's memory mechanism to generate motion- and spatially-aware predictions across the volume. Experiments on the OAI-ZIB dataset demonstrate a Dice similarity coefficient of 0.9643 on tibia using only a single prompt and no task-specific training or fine-tuning.

Generative models can now produce photorealistic synthetic data virtually indistinguishable from real training data, challenging OOD detectors that rely on generative model likelihoods due to likelihood misestimation and typicality issues. This paper introduces Forte, which hypothesizes that estimating typical sets using self-supervised learners leads to better OOD detection, using representation learning and informative summary statistics based on manifold estimation to address these issues. Forte outperforms other unsupervised approaches and achieves state-of-the-art performance on established challenging benchmarks as well as new synthetic data detection tasks, requiring no class labels.

This study proposes a novel unsupervised method for the fully automated segmentation of Bone Marrow Edema-like Lesions (BMEL) in knee MRI. By leveraging conditional diffusion models and anomaly detection, the approach eliminates the need for labor-intensive and bias-prone manual annotations. The research sets new benchmarks for BMEL segmentation performance and provides a more reliable, quantitative tool for early diagnosis and prognosis of knee osteoarthritis.