Multi-label segmentation of bone, muscle, and fat in CT volumes via convex relaxation

Abstract

The segmentation of bone, fat and muscle tissue in CT volumes is of special interest for surgeons and radiologists in order to diagnose some diseases and/or in surgery planning. These tissues are very difficult to delineate due to the presence of multiple and different structures and the similarity in terms of Hounsfield values with surrounding organs. In this paper, an automatic algorithm to implement the segmentation of bone, muscle and fat tissue is shown. The segmentation is carried out by minimizing an energy function via convex relaxation. In previous works carried out by the authors, only two labels had been considered (bone and muscle) and the methods had strong problems to segment skeletal muscle accurately due to the presence of internal organs with Hounsfield values similar to those within muscle tissue. In the present work, prior knowledge about the distribution of skeletal muscle in abdominal, chest and pelvis CT volumes has been considered by including a binary distance transform in the computation of the cost terms. Furthermore, a third label corresponding to fat tissue has been included in the segmentation. A public database has been used to assess the performance of the algorithm. Different metrics such as DICE, Jaccard, specificity, sensitivity and Positive Predictive Value (PPV) indexes have been obtained to evaluate the algorithm performance The technique has been compared with a previous multi-label scheme, a Hybrid level-set model implementation and a thresholding algorithm. The algorithm proposed outperformed the other methods in all the metrics considered.