FEM-based automatic segmentation of muscle and fat tissues from thoracic CT images

IEEE Trans. Med. Imaging

Cobzaş

Esfandiari

et al. 2016

Self Cite

115

112

The proportions of muscle and fat tissues in the human body, referred to as body composition is a vital measurement for cancer patients. Body composition has been recently linked to patient survival and the onset/recurrence of several types of cancers in numerous cancer research studies. This paper introduces a fully automatic framework for the segmentation of muscle and fat tissues from CT images to estimate body composition. We developed a novel finite element method (FEM) deformable model that incorporates a priori shape information via a statistical deformation model (SDM) within the template-based segmentation framework. The proposed method was validated on 1000 abdominal and 530 thoracic CT images and we obtained very good segmentation results with Jaccard scores in excess of 90% for both the muscle and fat regions.

Section: B Our Contributionsmentioning

confidence: 92%

Body Composition Assessment in Axial CT Images Using FEM-Based Automatic Segmentation of Skeletal Muscle

IEEE Trans. Med. Imaging

Cobzaş

Esfandiari

et al. 2016

Self Cite

115

112

“…Here, we address this problem and propose another approach to localize the L3 slice. Initial papers on automatic L3 CT segmentation proposed shape prior models for body composition analysis at L3 and at T4 vertebra levels (Chung et al, 2009;Popuri et al, 2013Popuri et al, , 2016. Recently, deep learning methods have been used for L3 tissue segmentation.…”

Section: Introductionmentioning

confidence: 99%

Deep learning method for localization and segmentation of abdominal CT

Dabiri

Computerized Medical Imaging and Graphics

et al. 2020

Self Cite

Computed Tomography (CT) imaging is widely used for studying body composition, i.e., the proportion of muscle and fat tissues with applications in areas such as nutrition or chemotherapy dose design. In particular, axial CT slices from the 3rd lumbar (L3) vertebral location are commonly used for body composition analysis. However, selection of the third lumbar vertebral slice and the segmentation of muscle/fat in the slice is a tedious operation if performed manually. The objective of this study is to automatically find the middle axial slice at L3 level from a full or partial body CT scan volume and segment the skeletal muscle (SM), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT) and intermuscular adipose tissue (IMAT) on that slice. The proposed algorithm includes an L3 axial slice localization network followed by a muscle-fat segmentation network. The localization network is a fully convolutional classifier trained on more than 12,000 images. The segmentation network is a convolutional neural network with an encoderdecoder architecture. Three datasets with CT images taken for patients with different types of cancers are used for training and validation of the networks. The mean slice error of 0.87± 2.54 was achieved for L3 slice localization on 1748 CT scan volumes. The performance of five class tissue segmentation network evaluated on two datasets with 1327 and 1202 test samples. The mean Jaccard score of 97% was achieved for SM and VAT tissue segmentation on 1327 images.

“…Muscle segmentation in L3 (Chung et al, 2009;Popuri et al, 2016) and in T4 (Popuri et al, 2013) using a shape prior modeling approach has been previously reported. Kamiya et al addressed the segmentation of individual muscle groups such as psoas major and rectus abdominis muscles from CT images by generating muscle group specific shape models (Kamiya et al, 2009;Kamiya et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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

Dabiri

Computerized Medical Imaging and Graphics

Feliciano

et al. 2019

Self Cite

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 generated by different CT devices and data acquisition settings. To ensure the model's robustness, each trained model was tested on all three available test sets. Errors and the effect of labeling protocol in these cases were analyzed and reported. The best performance of the proposed algorithm was achieved on 1327 L3 test samples with an overlap Jaccard score of 98% and sensitivity and specificity greater than 99%.