Pediatric Brain CT Image Segmentation Methods for Effective Age Prediction Models
Ren Morita, Saya Ando, Daisuke Fujita, Sho Ishikawa, Koji Onoue, Kumiko Ando, Reiichi Ishikura, Syoji Kobashi · 2022 World Automation Congress (WAC) · 2022
Brain imaging is used to diagnose pediatric brain diseases. However, there is no quantitative method to estimate developmental conditions such as underdevelopment or early growth, and qualitative diagnosis is based on the experience of skilled physicians. Therefore, we are developing a computer-aided diagnosis system to estimate brain age from pediatric brain CT images. This system segmented cranial regions from CT images and calibrated their posture and position. The system also extracts features from CT images using a 3D convolutional neural network (3D CNN) and predicts brain age using a fully connected layer. This paper focuses on the cranial region segmentation method, which is an essential analysis processing method for the system. We investigated two different methods of region segmentation, and a comparison experiment with 204 subjects aged 0 to 3 years (47 months) showed that we could improve 32% of the prediction accuracy of the 3D CNN model.