Joint segmentation of bones and muscles using an intensity and histogram-based energy minimization approach

摘要

Background and objectives: The segmentation of muscle and bone structures in CT is of interest to physicians and surgeons for surgical planning, disease diagnosis and/or the analysis of fractures or bone/muscle densities. Recently, the issue has been addressed in many research works. However, most studies have focused on only one of the two tissues and on the segmentation of one particular bone or muscle. This work addresses the segmentation of muscle and bone structures in 3D CT volumes.& para;& para;Methods: The proposed bone and muscle segmentation algorithm is based on a three-label convex relaxation approach. The main novelty is that the proposed energy function to be minimized includes distance to histogram models of bone and muscle structures combined with gray-level information.& para;& para;Results: 27 CT volumes corresponding to different sections from 20 different patients were manually segmented and used as ground-truth for training and evaluation purposes. Different metrics (Dice index, Jac-card index, Sensitivity, Specificity, Positive Predictive Value, accuracy and computational cost) were computed and compared with those used in some state-of-the art algorithms. The proposed algorithm outper-formed the other methods, obtaining a Dice coefficient of 0.88 +/- 0.14, a Jaccard index of 0.80 +/- 0.19, a Sensitivity of 0.94 +/- 0.15 and a Specificity of 0.95 +/- 0.04 for bone segmentation, and 0.78 +/- 0.12, 0.65 +/- 0.16, 0.94 +/- 0.04 and 0.95 +/- 0.04 for muscle tissue.& para;& para;Conclusions: A fast, generalized method has been presented for segmenting muscle and bone structures in 3D CT volumes using a multilabel continuous convex relaxation approach. The results obtained show that the proposed algorithm outperforms some state-of-the art methods. The algorithm will help physicians and surgeons in surgical planning, disease diagnosis and/or the analysis of fractures or bone/muscle densities. (C) 2017 Elsevier B.V. All rights reserved.