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A Robust Shape-Aware Rib Fracture Detection and Segmentation Framework with Contrastive Learning.

IEEE TRANSACTIONS ON MULTIMEDIA(2023)

Zhejiang Univ

Cited 24|Views21
Abstract
The rib fracture is a common type of thoracic skeletal trauma, and its inspections using computed tomography (CT) scans are critical for clinical evaluation and treatment planning. However, it is often challenging for radiologists to quickly and accurately detect rib fractures due to tiny objects and blurriness in large 3D CT images. Previous diagnoses for automatic rib fracture mostly relied on deep learning (DL)-based object detection, which highly depends on label quality and quantity. Moreover, general object detection methods did not take into consideration the typically elongated and oblique shapes of ribs in 3D volumes. To address these issues, we propose a shape-aware method based on DL called SA-FracNet for rib fracture detection and segmentation. First, we design a pixel-level pretext task founded on contrastive learning on massive unlabeled CT images. Second, we train the fine-tuned rib fracture detection model based on the pre-trained weights. Third, we develop a fracture shape-aware multi-task segmentation network to delineate the fracture based on the detection result. Experiments demonstrate that our proposed SA-FracNet achieves state-of-the-art rib fracture detection and segmentation performance on the public RibFrac dataset, with a detection sensitivity of 0.926 and segmentation Dice of 0.754. Test on a private dataset also validates the robustness and generalization of our SA-FracNet.
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Key words
Ribs,Computed tomography,Three-dimensional displays,Image segmentation,Task analysis,Training,Deep learning,Computer-aided diagnosis,rib fracture detection and segmentation,self-supervised contrastive learning,shape-aware model
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