Vestibule Segmentation from CT Images with Integration of Multiple Deep Feature Fusion Strategies

Vestibule Segmentation is of great significance for the clinical diagnosis of congenital ear malformations and cochlear implants. However, automated segmentation is a challenging task due to the tiny size, blur boundary, and drastic changes in shape and size. In this paper, a vestibule segmentation method from CT images has been proposed specifically, which exploits different deep feature fusion strategies, including convolutional feature fusion for different receptive fields, channel attention based feature channel fusion, and encoder-decoder feature fusion. The experimental results on the self-established vestibule segmentation dataset show that, compared with several state-of-the-art methods, our method can achieve superior segmentation accuracy.