Rupsa Bhattacharjee

Gabrielle Hoyer, Michelle W Tong, Rupsa Bhattacharjee et al.

Effective segmentation is fundamental for quantitative medical imaging; however, foundation segmentation models remain insufficiently evaluated for accuracy and biomarker fidelity across the diverse anatomical contexts and imaging protocols encountered in musculoskeletal (MSK) MRI. We evaluate three widely used segmentation models (SAM, SAM2, MedSAM) across eleven MSK MRI datasets spanning the knee, hip, spine, shoulder, and thigh. Our framework assesses both zero-shot and finetuned performance, with attention to segmentation accuracy, generalizability across imaging protocols, and reliability of derived quantitative biomarkers. Finetuned models showed consistent agreement with expert measurements for biomarkers including cartilage thickness, disc height, muscle volume, and compositional T1rho/T2 values. Automated prompting through the AutoLabel system enabled scalable segmentation, with moderate trade-offs in accuracy. As proof of concept, we applied the validated system to (i) a three-stage knee MRI triage cascade and (ii) a longitudinal landmark model that predicts total knee replacement and incident osteoarthritis. The framework offers a transparent method for benchmarking segmentation tools and connecting model performance to clinical imaging priorities.

Rupsa Bhattacharjee, Zehra Akkaya, Johanna Luitjens et al.

In the current study, our purpose is to evaluate the feasibility of applying deep learning (DL) enabled algorithms to quantify bilateral knee biomarkers in healthy controls scanned at 0.55T and compared with 3.0T. The current study assesses the performance of standard in-practice bone, and cartilage segmentation algorithms at 0.55T, both qualitatively and quantitatively, in terms of comparing segmentation performance, areas of improvement, and compartment-wise cartilage thickness values between 0.55T vs. 3.0T. Initial results demonstrate a usable to good technical feasibility of translating existing quantitative deep-learning-based image segmentation techniques, trained on 3.0T, out of 0.55T for knee MRI, in a multi-vendor acquisition environment. Especially in terms of segmenting cartilage compartments, the models perform almost equivalent to 3.0T in terms of Likert ranking. The 0.55T low-field sustainable and easy-to-install MRI, as demonstrated, thus, can be utilized for evaluating knee cartilage thickness and bone segmentations aided by established DL algorithms trained at higher-field strengths out-of-the-box initially. This could be utilized at the far-spread point-of-care locations with a lack of radiologists available to manually segment low-field images, at least till a decent base of low-field data pool is collated. With further fine-tuning with manual labeling of low-field data or utilizing synthesized higher SNR images from low-field images, OA biomarker quantification performance is potentially guaranteed to be further improved.