Automatic Segmentation of Skeletal Muscle Tissue in L3 CT Images Based on Random Forests and CNN Using Coarse Ground Truth Masks

Abstract

Estimates of the composition of skeletal muscle tissue (SMT) and adipose tissues are important in the treatment of debilitating diseases, such as cancer, and in the control of overweight and obesity. Several studies have shown a high correlation between the percentage of SMT in computed tomography (CT) images corresponding to the cross-section at the level of the third lumbar vertebra (L3) and the percentage of this tissue in the whole body. A large number of models has been proposed to automatically segment CT images in order to estimate tissue compositions, many of them use supervised Machine Learning (ML) methods, such as neural networks, which require large amounts of labeled images, i.e., images and ground truth masks obtained from manual segmentation by human experts. These large labeled datasets are not easily available to the public, thus the present work proposes a methodology capable of performing the automatic segmentation of SMT in single-slice CT images (at L3) using only “coarse” segmentation masks as ground truth in the ML algorithms’s training phases. By “coarse segmentation” we mean a semiautomated segmentation performed by a person without specialized knowledge of human anatomy. The proposed methodology oversegments the image into superpixels, which are classified by a Random Forest (RF) model. Then, a U-Net CNN refines the classification, using as input the pixels in the superpixel segments classified as SMT by the RF. The methodology achieved 99.21% of the accuracy obtained by the same CNN trained with golden standard ground truth masks, i.e., segmentation masks manually created by a medical expert.