Pelvic Bone Segmentation Guided Fracture Classification

Pelvic fractures are critical injuries that require timely and precise diagnosis. We are developing an AI-based automated computer-aided diagnosis (CAD) system to enhance the accuracy and reliability of pelvic fracture detection, ultimately supporting faster and more effective medical assessments.

The first step in our approach is multi-bone segmentation, a deep learning process that identifies and isolates the nine pelvic bones from the X-ray. The segmented bone masks are then aggregated to create a refined mask of the pelvic region. 

Next, we extract the segmented X-ray from the original X-ray using the aggregated mask. This helps the system focus only on relevant areas instead of the entire X-ray. We then feed it into a separate deep learning model for classification. This classification model determines whether a fracture is present.

To improve interpretability, we use GradCAM visualization, which highlights the critical areas the AI focuses during detection. This ensures the model’s decisions are transparent and aligned with anatomical relevance.

Relevant publications:

S. A. Ul Alam, S. Binte Alam, S. Saha, M. Haque, R. Rahman and S. Kobashi, “Pelvic bone region segmentation (PBRS) from X-ray image using convolutional neural network (CNN),” 2023 26th International Conference on Computer and Information Technology (ICCIT), Cox’s Bazar, Bangladesh, 2023, pp. 1-6, doi: 10.1109/ICCIT60459.2023.10441155.