Automatic rib fracture detection on postmortem CT data using deep learning.
Authors
Affiliations (7)
Affiliations (7)
- University Centre of Legal Medicine Lausanne-Geneva, University of Geneva, Rue Michel-Servet 1, 1206, Geneva, Switzerland. [email protected].
- Department of Computer Science, Viper Group, University of Geneva, Route de Drize 7, 1227, Carouge, Switzerland. [email protected].
- University Centre of Legal Medicine Lausanne-Geneva, University of Geneva, Rue Michel-Servet 1, 1206, Geneva, Switzerland.
- Department of Diagnostic and Interventional Radiology, University Hospital Lausanne, Lausanne, Switzerland.
- Institute of Informatics, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University Hospital Lausanne, Lausanne, Switzerland.
- Department of Computer Science, Viper Group, University of Geneva, Route de Drize 7, 1227, Carouge, Switzerland.
Abstract
To assess the performance of automatic rib fracture detection of an existing deep learning (DL) model, nnDetection, on a postmortem (PM) CT scan dataset, and to identify the main factors of domain shift between clinical and PM CT imaging. Rib fracture detection and classification in forensic investigations is a time-consuming yet crucial task that can contribute to determine the cause of death. DL models are a promising tool, as recent research shows that radiologists using DL models can detect rib fractures in clinical CT scans at higher sensitivity and in shorter time. A dataset of 50 PMCT scans (24% women; age: mean 61, range 19 - 96 years) was retrospectively collected and annotated, and used to train a first instance of the model, nnDetPM. Another instance of the model, nnDetClin, was trained on data from another dataset, RibFrac, consisting of 660 clinical CT scans (36% women; age: mean 55, range 21 - 94 years). On the PM testing set, nnDetPM achieved an average sensitivity of 70.2% and an average precision (at 0.1 intersection over union) of 78.1%, whereas nnDetClin fell far behind at 19.8% average sensitivity and 25.5% average precision, indicating a substantial impact of the domain shift from clinical to PM CT data. Further inspection of the results showed that the main factors of this domain shift were the position of the arms and the presence of medical ware in the image acquisition area of the PMCT scans. The performance of nnDetPM, with an average sensitivity of 70.2%, was notable and comparable to that of radiologists. However, more advanced techniques must be explored to decide if DL models can overcome the domain shift factors.