Hybrid Deep Feature Fusion of 2D CNN and 3D CNN for Vestibule Segmentation from CT Images

Abstract

The accurate vestibule segmentation from CT images is essential to the quantitative analysis of the anatomical structure of the ear. However, it is a challenging task due to the tiny size, blur boundary, and drastic variations in shape and size. In this paper, according to the specific characteristics and segmentation requirements of the vestibule, a vestibule segmentation network with a hybrid deep feature fusion of 2D CNN and 3D CNN is proposed. First, a 2D CNN is designed to extract the intraslice features through multiple deep feature fusion strategies, including a convolutional feature fusion strategy for different receptive fields, a feature channel fusion strategy based on channel attention mechanism, and an encoder-decoder feature fusion strategy. Next, a 3D DenseUNet is designed to extract the interslice features. Finally, a hybrid feature fusion module is proposed to fuse the intraslice and interslice features to effectively exploit the context information, thus achieving the accurate segmentation of the vestibule structure. At present, there is no publicly available dataset for vestibule segmentation. Therefore, the proposed segmentation method is validated on two self-established datasets, namely, VestibuleDataSet and IEBL-DataSet. It has been compared with several state-of-the-art methods on the datasets, including the general DeeplabV3+ method and specific 3D DSD vestibule segmentation method. The experimental results show that our proposed method can achieve superior segmentation accuracy.