Vestibule segmentation from CT images with integration of multiple deep feature fusion strategies

•A vestibule segmentation method from CT images has been proposed specifically.•A new network architecture based on multiple deep feature fusion strategies.•The research results show that designing a specific network architecture according to the characteristics of the vestibule can achieve better segmentation performance. 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.