Stepwise deep neural network (stepwise-net) for head and neck auto-segmentation on CT images. (April 2022)

• Record Type: Journal Article
• Authors: Kawahara, Daisuke; Tsuneda, Masato; Ozawa, Shuichi; Okamoto, Hiroyuki; Nakamura, Mitsuhiro; Nishio, Teiji; Saito, Akito; Nagata, Yasushi
• Is Part Of: Computers in biology and medicine. Volume 143(2022)
• Publication Date: 2022-04
• Subjects: Deep learning -- Segmentation -- CNN; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.105295
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Objective: The current study aims to propose the auto-segmentation model on CT images of head and neck cancer using a stepwise deep neural network (stepwise-net). Material and methods: Six normal tissue structures in the head and neck region of 3D CT images: Brainstem, optic nerve, parotid glands (left and right), and submandibular glands (left and right) were segmented with deep learning. In addition to a conventional convolutional neural network (CNN) on U-net, a stepwise neural network (stepwise-network) was developed. The stepwise-network was based on 3D FCN. We designed two networks in the stepwise-network. One is identifying the target region for the segmentation with the low-resolution images. Then, the target region is cropped, which used for the input image for the prediction of the segmentation. These were compared with a clinical used atlas-based segmentation. Results: The DSCs of the stepwise-net was significantly higher than the atlas-based method for all organ at risk structures. Similarly, the JSCs of the stepwise-net was significantly higher than the atlas-based methods for all organ at risk structures. The Hausdorff distance (HD) was significantly smaller than the atlas-based method for all organ at-risk structures. For the comparison of the stepwise-net and U-net, the stepwise-net had a higher DSC and JSC and a smaller HD than the conventional U-net. Conclusions: We found that the stepwise-network plays a role is superior to conventional U-net-based and atlas-based segmentation. Our proposed model that is a potentially valuable method for improving the efficiency of head and neck radiotherapy treatment planning. Highlights: ART needs automated delineation for treatment planning. Step-wise network can reduce the consumption of memory in deep learning. Stepwise-network plays a role is superior to conventional deep learning method. Our proposed model is potentially valuable method for radiotherapy treatment planning.