Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence
Motivation: Morphometric assessment of cartilage(e.g.,thickness), through MRI yields accurate measurements on the progression of Osteoarthritis(OA). Such quantitative measurements require image segmentation techniques. Recent developments in Visual Foundational Models(VFM) bring opportunities to increasing generality and robustness.
Goal(s): What improvements can VFM-based approaches bring to automatic segmentation of knee 3DMRIs, and how it compares to traditional convolution networks(CNNs)?
Approach: Trained 2DVFM, 3DCNN, and a modified 3DVFM on 500MRI volumes. Evaluated qualitative and quantitatively on external datasets.
Results: The proposed 3D-VFM, demonstrates a slight advantage on quantitative morphological assessment, but strongly outperforms others when qualitatively assessed by radiologists, presenting a promising direction and better generalization.
Impact: By leveraging Visual Foundational Models (VFM) in the morphometric assessment of cartilage through 3D MRIs, our research demonstrates significant promise in enhancing the accuracy and generalization of knee segmentation to be applied to osteoarthritis progression measurements.
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