Automatic segmentation of large-scale CT image datasets for detailed body composition analysis

BMC Bioinformatics

Research

Göran Bergström¹  Nouman Ahmad²  Björn Sparresäter²  Sambit Tarai²  Elin Lundström²  Joel Kullberg³  Håkan Ahlström³  Robin Strand⁴ 

[1] Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden;Department of Clinical Physiology, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden;Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden;Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden;Antaros Medical, Mölndal, Sweden;Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden;Department of Information Technology, Uppsala University, Uppsala, Sweden;

关键词: Deep learning;    Segmentation;    Medical imaging;    Computed tomography;    Body composition;   

DOI  :  10.1186/s12859-023-05462-2

 received in 2023-03-27, accepted in 2023-09-01,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundBody composition (BC) is an important factor in determining the risk of type 2-diabetes and cardiovascular disease. Computed tomography (CT) is a useful imaging technique for studying BC, however manual segmentation of CT images is time-consuming and subjective. The purpose of this study is to develop and evaluate fully automated segmentation techniques applicable to a 3-slice CT imaging protocol, consisting of single slices at the level of the liver, abdomen, and thigh, allowing detailed analysis of numerous tissues and organs.MethodsThe study used more than 4000 CT subjects acquired from the large-scale SCAPIS and IGT cohort to train and evaluate four convolutional neural network based architectures: ResUNET, UNET++, Ghost-UNET, and the proposed Ghost-UNET++. The segmentation techniques were developed and evaluated for automated segmentation of the liver, spleen, skeletal muscle, bone marrow, cortical bone, and various adipose tissue depots, including visceral (VAT), intraperitoneal (IPAT), retroperitoneal (RPAT), subcutaneous (SAT), deep (DSAT), and superficial SAT (SSAT), as well as intermuscular adipose tissue (IMAT). The models were trained and validated for each target using tenfold cross-validation and test sets.ResultsThe Dice scores on cross validation in SCAPIS were: ResUNET 0.964 (0.909–0.996), UNET++ 0.981 (0.927–0.996), Ghost-UNET 0.961 (0.904–0.991), and Ghost-UNET++ 0.968 (0.910–0.994). All four models showed relatively strong results, however UNET++ had the best performance overall. Ghost-UNET++ performed competitively compared to UNET++ and showed a more computationally efficient approach.ConclusionFully automated segmentation techniques can be successfully applied to a 3-slice CT imaging protocol to analyze multiple tissues and organs related to BC. The overall best performance was achieved by UNET++, against which Ghost-UNET++ showed competitive results based on a more computationally efficient approach. The use of fully automated segmentation methods can reduce analysis time and provide objective results in large-scale studies of BC.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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