Muscle segmentation in axial computed tomography (CT) images at the lumbar (L3) and thoracic (T4) levels for body composition analysis

• Published in: Computerized medical imaging and graphics 2019-07, Vol.75, p.47-55
• Main Authors: Dabiri, Setareh, Popuri, Karteek, Cespedes Feliciano, Elizabeth M., Caan, Bette J., Baracos, Vickie E., Beg, Mirza Faisal
• Format: Article
• Language: English
• Subjects: Aging; Algorithms; Artificial neural networks; Body composition; Body fluids; Bone mass; Cachexia; Cancer; Computational fluid dynamics; Computed tomography; Convolutional neural network; CT imaging; Data acquisition; Datasets; Electronic devices; Image processing; Image segmentation; In vivo methods and tests; Medical imaging; Model testing; Muscles; Musculoskeletal system; Neural networks; Sarcopenia; Skeletal muscle; Skeletal muscle segmentation; Test sets; Thorax; Tomography
• ISSN: 0895-6111; 1879-0771
• DOI: 10.1016/j.compmedimag.2019.04.007

•A new convolutional neural network is presented that takes as input an axial slice from a CT image at L3 or T4 level and generates the muscle segmentation mask of the image in almost real time (it takes less than one second (∼200 ms) for the trained network to generate the muscle mask).•The performance of the network on three large datasets is evaluated and demonstrates high Jaccard scores in the range of 0.96–0.98 on these datasets.•We validated the model's robustness by reporting the performance of the model on three different (and unseen) datasets generated by different CT devices and data acquisition settings, males and females, with a variety of muscle tissue shape and form, in various cancers attesting to the generalizability of the result.•In total more than 9000 L3 images were used for investigating (train/test) the proposed model. This is considerably higher than the number of images used for validating the methods in other papers in the literature attesting to robustness of the results.•The model trained on the large set of L3 is fine-tuned for T4 muscle segmentation. The model's performance is investigated with various experiments considering different ratio of test and training images and we find that even with a small number of images at the T4 level, having a model trained at the L3 level provides a very strong initialization to develop an accurate model for the T4 level. This indicates future generalizability to other axial locations in the CT image stack. In diseases such as cancer, patients suffer from degenerative loss of skeletal muscle (cachexia). Muscle wasting and loss of muscle function/performance (sarcopenia) can also occur during advanced aging. Assessing skeletal muscle mass in sarcopenia and cachexia is therefore of clinical interest for risk stratification. In comparison with fat, body fluids and bone, quantifying the skeletal muscle mass is more challenging. Computed tomography (CT) is one of the gold standard techniques for cancer diagnostics and analysis of progression, and therefore a valuable source of imaging for in vivo quantification of skeletal muscle mass. In this paper, we design a novel deep neural network-based algorithm for the automated segmentation of skeletal muscle in axial CT images at the third lumbar (L3) and the fourth thoracic (T4) levels. A two-branch network with two training steps is investigated. The network's performance is evaluated for three trained models on separate datasets. These datasets were