Deep learning for abdominal adipose tissue segmentation with few labelled samples

• Published in: International journal for computer assisted radiology and surgery Vol. 17; no. 3; pp. 579 - 587
• Main Authors: Wang, Zheng, Hounye, Alphonse Houssou, Zhang, Jianglin, Hou, Muzhou, Qi, Min
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.03.2022; Springer Nature B.V
• Subjects: Abdomen; Abdominal Fat - diagnostic imaging; Adipose tissue; Anatomy; Artificial neural networks; Automation; Body fat; Computed tomography; Computer Imaging; Computer Science; Deep Learning; Health Informatics; Humans; Imaging; Medical imaging; Medicine; Medicine & Public Health; Neural Networks, Computer; Original Article; Pattern Recognition and Graphics; Radiology; Semantic segmentation; Subcutaneous Fat; Surgery; Tomography, X-Ray Computed; Vision; Subcutaneous adipose tissue (SAT); Visceral adipose tissue (VAT); Convolutional neural network (ConvNet); Computed tomography (CT)
• DOI: 10.1007/s11548-021-02533-8
• ISSN: 1861-6410; 1861-6429

Purpose Fully automated abdominal adipose tissue segmentation from computed tomography (CT) scans plays an important role in biomedical diagnoses and prognoses. However, to identify and segment subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) in the abdominal region, the traditional routine process used in clinical practise is unattractive, expensive, time-consuming and leads to false segmentation. To address this challenge, this paper introduces and develops an effective global-anatomy-level convolutional neural network (ConvNet) automated segmentation of abdominal adipose tissue from CT scans termed EFNet to accommodate multistage semantic segmentation and high similarity intensity characteristics of the two classes (VAT and SAT) in the abdominal region. Methods EFNet consists of three pathways: (1) The first pathway is the max unpooling operator, which was used to reduce computational consumption. (2) The second pathway is concatenation, which was applied to recover the shape segmentation results. (3) The third pathway is anatomy pyramid pooling, which was adopted to obtain fine-grained features. The usable anatomical information was encoded in the output of EFNet and allowed for the control of the density of the fine-grained features. Results We formulated an end-to-end manner for the learning process of EFNet, where the representation features can be jointly learned through a mixed feature fusion layer. We immensely evaluated our model on different datasets and compared it to existing deep learning networks. Our proposed model called EFNet outperformed other state-of-the-art models on the segmentation results and demonstrated tremendous performances for abdominal adipose tissue segmentation. Conclusion EFNet is extremely fast with remarkable performance for fully automated segmentation of the VAT and SAT in abdominal adipose tissue from CT scans. The proposed method demonstrates a strength ability for automated detection and segmentation of abdominal adipose tissue in clinical practise.