The challenge of detecting impact fracture of the pelvis in CT images using the CNN model

Fragility fractures of the pelvis (FFP) often occur because of the osteoporosis in the elderly, and are difficult to found on transverse CT images. It desires an automatic pelvic fracture detection system for improving quality of medicine. Our previous study, which is based on 3D analysis using CT images of the whole pelvis, achieved a precision-recall area-under-the-curve (PR-AUC) of 0.824. However, the recall of impact fractures is relatively low (0.350). To overcome this difficulty, we have previously introduced a novel approach of complementary detecting pelvic impact fractures, called boring survey based fracture detection (BSFD). In order to improve the performance of detecting impact fractures based on BSFD, this study proposes a new 3D CNN model with considering the 3D feature architecture. The detection accuracy is improved by eliminating the separated fractures from the pelvic surface. The separated fractures are detected by our previous method. Also, with respect to the imbalance problem, the fractured regions are over-sampled in training data. To evaluate the proposed method, this study recruited 28 patients as the training data and 4 patients as the validation data. It achieved a receiver operating characteristic AUC (ROC-AUC) of 0.878 and a recall of 0.806 for detecting impact fractures for the validation data. We can also three-dimensionally visualize the estimated degree of fractures for a sake of easy understanding by physicians.