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Found 4 papers, 2 papers with code
Data-Limited Tissue Segmentation using Inpainting-Based Self-Supervised Learning
Although supervised learning has enabled high performance for image segmentation, it requires a large amount of labeled training data, which can be difficult to obtain in the medical imaging field.
Open source software for automatic subregional assessment of knee cartilage degradation using quantitative T2 relaxometry and deep learning
Subregional T2 values and four-year changes were calculated using a musculoskeletal radiologist's segmentations (Reader 1) and the model's segmentations.
The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset
Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression.
Technical Considerations for Semantic Segmentation in MRI using Convolutional Neural Networks
High-fidelity semantic segmentation of magnetic resonance volumes is critical for estimating tissue morphometry and relaxation parameters in both clinical and research applications.
