Fully Automated Segmentation of Connective Tissue Compartments for CT-Based Body Composition Analysis: A Deep Learning Approach. Issue 6 (June 2020)

• Record Type: Journal Article
• Authors: Nowak, Sebastian; Faron, Anton; Luetkens, Julian A.; Geißler, Helena L.; Praktiknjo, Michael; Block, Wolfgang; Thomas, Daniel; Sprinkart, Alois M.
• Is Part Of: Investigative radiology. Volume 55:Issue 6(2020)
• Publication Date: 2020-06
• Subjects: body compartment analysis -- deep learning -- Convolutional Neural Network -- automated segmentation -- computed tomography -- DeepMedic; Diagnosis, Radioscopic -- Periodicals; Radiology, Medical -- Periodicals; 616.0757
• DOI: 10.1097/RLI.0000000000000647
• Languages: English
• ISSNs: 0020-9996

Abstract

Abstract : Objective: Body composition comprises prognostic information in patients with various malignancies and can be opportunistically determined from routine computed tomography (CT) scans. However, accurate assessment of patients with alterations, for example, due to ascites or anasarca, and accurate identification of intermuscular fat remain challenging. In this study, we aimed to develop a fully automated and highly accurate segmentation tool for connective tissue compartments from abdominal CT scans using the open-source Convolutional Neural Network (CNN) DeepMedic. Materials and Methods: In this retrospective study, a CNN was developed using data of 1143 consecutive patients undergoing either preinterventional CT for transcatheter aortic valve implantation (TAVI) (82%) or diagnostic CT for liver cirrhosis with portosystemic shunting (PTSS) (18%). All analyses were performed on single-slice images at the L3/L4 level. The data were subdivided into subsets of training (70%), validation (15%), and test data (15%), balanced for TAVI and PTSS patients. To demonstrate the generalizability of the applied method with respect to nonspecific clinical routine data, the model with the highest performance in TAVI and PTSS patients was further tested on 100 randomly selected patients who underwent CT for routine diagnostic purposes at a hospital of maximum care, including critically ill patients. The applicability of the method to native CT examinations was additionally tested on 50 patients. Results: Compared with the ground truth of the test data, the presented method achieved highly accurate segmentation results (subcutaneous adipose tissue [SAT], Dice score [DSC]: 0.98 ± 0.01; visceral adipose tissue [VAT], DSC: 0.96 ± 0.04; skeletal muscles [SM], DSC: 0.95 ± 0.02) and showed excellent generalizability on the routine CT diagnostic patients (SAT, DSC: 0.97 ± 0.04; VAT, DSC: 0.95 ± 0.05; SM, DSC: 0.95 ± 0.04) and also on native CT scans (SAT, DSC: 0.99 ± 0.01; VAT, DSC: 0.97 ± 0.03; SM, DSC: 0.97 ± 0.02). Conclusions: Fully automated determination of body composition based on CT can be performed with excellent results using the open-source CNN DeepMedic. The trained model is made usable for research by a deployable and sharable application. Abstract : Supplemental digital content is available in the text.