Fragility fracture detection with 3D CT images in the pelvis like the boring survey

• Published in: Transactions of Japanese Society for Medical and Biological Engineering Vol. Annual59; no. Abstract; p. 296
• Main Authors: Muratsu, Hirotsugu, Kobashi, Shoji, Maruo, Akihiro, Rashedur, Rahman, Yamamoto, Naoto, Hayashi, Keigo, Yagi, Naomi
• Format: Journal Article
• Language: Japanese
• Published: Japanese Society for Medical and Biological Engineering 2021; 公益社団法人 日本生体医工学会
• ISSN: 1347-443X; 1881-4379
• DOI: 10.11239/jsmbe.Annual59.296

In Japan of a super-aging society, the number of patients suffering from fragility fracture of the pelvis (FFP) with osteoporosis is increasing. It can make patients bedridden and the complication. Physicians diagnose the fracture in 3D CT images, which is hard and time-consuming to find FFP. This paper proposes a novel method of boring survey based fracture detection (BSFD), to automatically detect FFP in 3D CT images. Firstly, the bone surface of the pelvis is extracted from CT images. Then, it bores the quadratic prism for the internal bone area with CT values. The 3D convolutional neural network can predict a probability of fracture, and apply to the whole pelvis. The method was evaluated by using 110 elderly subjects with pelvic fractures. The AUC was 0.84 for training subjects and 0.77 for evaluation subjects.In addition, it is useful for physicians to display the 3D distribution of fracture possibilities.