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Abstract #3584

Quantification of Inter-Muscular Adipose Infiltration in Calf/Thigh MRI using Fully and Weakly Supervised Semantic Segmentation

Rula Amer1, Jannette Nassar1, David Bendahan2, Hayit Greenspan1, and Noam Ben-Eliezer1,3,4
1Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel, 2CNRS, CRMBM, Aix Marseille University, Marseille, France, 3Center for Advanced Imaging Innovation and Research, New York University, New York, NY, United States, NY, United States, 4Sagol School of Neuroscience, Tel Aviv University, Ramat-Aviv, Israel

Quantification of subcutaneous fat infiltration in diseased muscle regions holds great prognostic value as it helps monitor the progression of muscular dystrophies. To estimate the infiltration, two stages are performed. The first isolates the region of muscle from the thigh/calf anatomy using U-net architecture for fully supervised segmentation. The second stage classifies muscle into diseased/healthy pixels using a weakly supervised segmentation method, incorporating a deep convolutional auto-encoder with triplet loss, and creates two clusters in the embedded feature space using k-means. The results showed a high Dice coefficient and a strong correlation between the fat infiltration and disease severity level.

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