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)

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 tradition...