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http://dx.doi.org/10.9717/kmms.2022.25.5.757

A Study on the Liver and Tumor Segmentation and Hologram Visualization of CT Images Using Deep Learning  

Kim, Dae Jin (Department of Health Sciences and Technology, GAIHST, Gachon University)
Kim, Young Jae (Gachon University School of Medicine)
Jeon, Youngbae (Department of Surgery, Gachon University Gil Medical Center)
Hwang, Tae-sik (Department of Surgery, Gachon University Gil Medical Center)
Choi, Seok Won (Department of Surgery, Gachon University Gil Medical Center)
Baek, Jeong-Heum (Department of Surgery, Gachon University Gil Medical Center)
Kim, Kwang Gi (Gachon University School of Medicine)
Publication Information
Journal of Korea Multimedia Society / v.25, no.5, 2022 , pp. 757-768 More about this Journal
Abstract
In this paper, we proposed a system that visualizes a hologram device in 3D by utilizing the CT image segmentation function based on artificial intelligence deep learning. The input axial CT medical image is converted into Sagittal and Coronal, and the input image and the converted image are divided into 3D volumes using ResUNet, a deep learning model. In addition, the volume is created by segmenting the tumor region in the segmented liver image. Each result is integrated into one 3D volume, displayed in a medical image viewer, and converted into a video. When the converted video is transmitted to the hologram device and output from the device, a 3D image with a sense of space can be checked. As for the performance of the deep learning model, in Axial, the basic input image, DSC showed 95.0% performance in liver region segmentation and 67.5% in liver tumor region segmentation. If the system is applied to a real-world care environment, additional physical contact is not required, making it safer for patients to explain changes before and after surgery more easily. In addition, it will provide medical staff with information on liver and liver tumors necessary for treatment or surgery in a three-dimensional manner, and help patients manage them after surgery by comparing and observing the liver before and after liver resection.
Keywords
Deep Learning; Liver Segmentation; Artificial Intelligence; Hologram;
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Times Cited By KSCI : 1  (Citation Analysis)
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