Meeting Banner
Abstract #0258

Deep Learning Based Segmentation and Fat Fraction Assessment of the Calf in Diabetic Subjects and Non-Diabetic Controls

Jill T Shah1, Katherine Medina2,3, Haresh R Rajamohan2,4, Justin Ho2,3, Cem M Deniz2,3, and Ryan Brown2,3
1New York University Grossman School of Medicine, New York, NY, United States, 2Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 3Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 4Center for Data Science, New York University, New York, NY, United States

Diabetes mellitus, muscular dystrophies, and other pathologies are characterized by metabolic impairment that can lead to lower extremity muscle degeneration. While MRI provides access to quantitative biomarkers to characterize muscle quality, analysis requires time-consuming manual image segmentation. To address this problem, we developed an automated segmentation algorithm based on a convolutional neural network that provided high dice similarity coefficient scores (>0.92) in the gastrocnemius medial, gastrocnemius lateral, and soleus muscles. We utilized the automatic segmentations to show volumetric fat fraction was elevated in individuals with diabetic peripheral neuropathy compared to controls in the soleus and gastrocnemius medial muscles (P<0.05).

This abstract and the presentation materials are available to members only; a login is required.

Join Here