Deep Learning-Based Automatic Detection and Classification of Rib Fractures from CT scans

Master thesis (2023)

Authors

E.N. Borren Mechanical Engineering

Contributors

Theo Van Walsum Erasmus MC (mentor)
Mathieu M.E. Wijffels Erasmus MC (mentor)
M. van der Elst Support Biomechanical Engineering (graduation committee member)
Faculty
Mechanical Engineering, Mechanical Engineering
Copyright: © 2023 Noor Borren
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Published Date

26-09-2023

Language

English

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Faculty

Mechanical Engineering

Abstract

Introduction: Trauma-induced rib fractures are a common injury, affecting millions of individuals globally each year. Although anteroposterior thoracic radiographs are part of the standard posttraumatic screening, the most sensitive modality, and therefore golden standard for diagnosing rib fractures, is computed tomography (CT). Still, between 19.2% and 26.8% of rib fractures are missed. Another problem encountered in rib fracture treatment management is the large interobserver variability on their taxonomy. This thesis aims to automate rib fracture detection and improve consistency in their classification by developing a Deep Learning (DL) model, using CT data.

Methods: The rib fractures were classified according to the Chest Wall Injury Society (CWIS) taxonomy, evaluating rib fracture’s type, displacement and location. Furthermore, the ribs were numbered from 1 up to and including 12 from cranio-caudal direction. For the detection and three CWIS labels, three classification models of the nnDetection framework were trained. The rib numbering consisted of a trained nnU-Net segmentation model. The four models were combined to obtain the proposed DCRibFrac model.

Experiments and results: The dataset is composed of retrospectively collected and anonymized CT scans of 100 randomly selected patients (1010 rib fractures) who were admitted to the Erasmus MC following blunt chest trauma. On the internal test set, DCRibFrac achieved a detection sensitivity of 77%, precision of 79%, and F1-score of 78%, with a mean false-positives per scan of 2.26. The type labels had the lowest scores, with sensitivities between 17% and 90%. The displacement labels had sensitivities between 43% and 91%. The location labels had the highest scores, with sensitivities between 88% and 96%. The rib number was correct in 72% of the rib fractures when wrong segmentations were excluded.

Conclusion: The proposed DL model automates acute rib fracture detection and reaches a sensitivity that is on par with clinicians. This model is the first, to the authors’ knowledge, to incorporate the CWIS taxonomy and shows its potential for achieving a consistent classification.