Automatic Body Region Classification in CT Scans Using Deep Learning.
Authors
Affiliations (7)
Affiliations (7)
- Faculty of Engineering & Applied Science, Memorial University of Newfoundland, St. John's, NL, Canada. [email protected].
- Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada.
- Faculty of Engineering, McMaster University, Hamilton, ON, Canada.
- Faculty of Engineering & Applied Science, Memorial University of Newfoundland, St. John's, NL, Canada.
- Diagnostic Imaging Department, Warmian-Masurian Cancer Center, Ministry of the Interior and Administration's Hospital, and Department of Oncology, University of Warmia and Mazury, Olsztyn, Poland.
- School of Engineering, Simon Fraser University, Burnaby, BC, Canada.
- Department of Computer Science, Memorial University of Newfoundland, St. John's, NL, Canada.
Abstract
Accurate classification of anatomical regions in computed tomography (CT) scans is essential for optimizing downstream diagnostic and analytic workflows in medical imaging. We demonstrate the high performance that deep learning (DL) algorithms can achieve in the classification of whole-body parts in CT images acquired under various protocols. Our model was trained using a dataset consisting of 5485 anonymized neuroimaging informatics technology initiative (NIFTI) CT scans collected from 45 different health centers. The dataset was split into 3290 scans for training, 1097 scans for validation, and 1098 scans for testing. Each body CT scan was classified into six distinct classes covering the whole body: chest, abdomen, pelvis, chest and abdomen, abdomen and pelvis, and chest and abdomen and pelvis. The performance of the DL model stood at an accuracy, precision, recall, and F1-score of 97.53% (95% CI: 96.62%, 98.45%), 97.56% (95% CI: 96.6%, 98.4%), 97.6% (95% CI: 96.7%, 98.5%), and 97.56% (96.6%, 98.4%), respectively, in identifying different body parts. These findings demonstrate the strength of our approach in annotating CT images through a wide variation in both acquisition protocols and patient demographics. This study underlines the potential that DL holds for medical imaging and, in particular, for the automation of body region classification in CT. Our findings confirm that these models could be implemented in clinical routines to improve diagnostic efficiency and harmony.