MuViSS : Muscle, Visceral and Subcutaneous Segmentation by an automatic evaluation method using Deep Learning

Purpose: Patient body composition is a major factor in patient management. Indeed, assessment of SMI as well as VFA and, to a lesser extent, SFA is a major factor in patient survival, particularly in surgery. However, to date, there is no simple, rapid, open-access assessment method. The aim of this work is to provide a simple, rapid and accurate tool for assessing patients' body composition. Material and methods: A total of 343 patients underwent liver transplantation at the University Hospital of Rennes between January 1st, 2012 and December 31s, 2018. Image analysis was performed using the open source software ImageJ. Tissue distinction was based on Hounsfield density. The training dataset used 332 images (320 for training and 12 for validation). The model was evaluated on 11 patients. The complete software and video package is available at https://github.com/tpecot/MuViSS. Results: In total, the model was trained with 332 images and evaluated on 11 images. Model accuracy is 0.974 (SD 0.003), Jaccard's index is 0.98 for visceral fat, 0.895 for muscle and 0.94 for subcutaneous fat. The Dice index is 0.958 (SD 0.003) for visceral fat, 0.944 (SD: 0.012) for muscle and 0.970 (SD: 0.013) for subcutaneous fat. Finally, the Normalized root mean square error is 0.007 for visceral fat, 0.0518 for muscle and 0.0124 for subcutaneous fat. Conclusion: To our knowledge, this is the first freely available model for assessing body composition. The model is fast, simple and accurate, based on Deep Learning.