A 3D deep supervised densely network for small organs of human temporal bone segmentation in CT images. (April 2020)

• Record Type: Journal Article
• Authors: Li, Xiaoguang; Gong, Zhaopeng; Yin, Hongxia; Zhang, Hui; Wang, Zhenchang; Zhuo, Li
• Is Part Of: Neural networks. Volume 124(2020)
• Page Start: 75
• Page End: 85
• Publication Date: 2020-04
• Subjects: Computed tomography imaging analysis -- Temporal bone -- Deep supervised densely network; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2020.01.005
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: Computed Tomography (CT) has become an important way for examining the critical anatomical organs of the human temporal bone in the diagnosis and treatment of ear diseases. Segmentation of the critical anatomical organs is an important fundamental step for the computer assistant analysis of human temporal bone CT images. However, it is challenging to segment sophisticated and small organs. To deal with this issue, a novel 3D Deep Supervised Densely Network (3D-DSD Net) is proposed in this paper. The network adopts a dense connection design and a 3D multi-pooling feature fusion strategy in the encoding stage of the 3D-Unet, and a 3D deep supervised mechanism is employed in the decoding stage. The experimental results show that our method achieved competitive performance in the CT data segmentation task of the small organs in the temporal bone.