COMPARATIVE ANALYSIS OF 3D CNN, NNU-NET, AND SAM2-BASED MODELS FOR KNEE MRI SEGMENTATION.
Fine-tuned MedSAM2 greatly improved meniscus segmentation accuracy over pretrained MedSAM2
COMPARATIVE ANALYSIS OF 3D CNN, NNU-NET, AND SAM2-BASED MODELS FOR KNEE MRI SEGMENTATION.
MRI enables assessment of morphological and compositional changes in knee cartilage and meniscus relevant to osteoarthritis (osteoarthritis) progression.
1) To evaluate and compare the performance of domain-specific fine-tuned MedSAM2 and nnU-Net for automated multi-structure knee MRI segmentation using all OAI baseline cohort.
Sagittal DESS knee MR images from the OAI baseline dataset (n = 9,592 MRI of left and right knees) and corresponding segmentation masks were used.
Using the entire OAI baseline dataset (Objective 1), KneeSAM2 achieved a mean Dice of 0.92 and IoU of 0.88, surpassing pretrained MedSAM2 by 0.20 in Dice and 0.23 in IoU.
The nnU-Net model, trained on the same IWOAI data splits, achieved the highest performance overall (Dice: femoral cartilage 0.95, tibial cartilage 0.95, patellar cartilage 0.93, meniscus 0.94), surpassing all other models.
Domain-specific fine-tuning improved MedSAM2 performance for segmenting small and anatomically complex knee structures, supporting the need for targeted adaptation of transformer-based foundation models (Objective 1).
However, on the IWOAI Challenge benchmark (Objective 2), fine-tuned MedSAM2 showed competitive but variable performance across structures, whereas nnU-Net consistently achieved higher accuracy.