Paper
13 May 2022 Colorectal segmentation in medical images
Yichao Xie
Author Affiliations +
Proceedings Volume 12248, Second International Conference on Sensors and Information Technology (ICSI 2022); 122480U (2022) https://doi.org/10.1117/12.2637414
Event: 2nd International Conference on Sensors and Information Technology (ICSI 2022), 2022, Nanjing, China
Abstract
Colorectal cancer is one of the digestive tract malignant tumors in life. In recent years, the incidence rate of rectal cancer has increased significantly in China. The segmentation of CT images of colorectal tumors is a tedious, time-consuming and expensive job.When diagnosing rectal cancer, it would be helpful to accurately segment the rectal tumor region from CT images so that doctors could make more accurate diagnoses and thus have better results for the disease. Based on the literature review, this paper presents medical image segmentation algorithms, including traditional algorithms based on energy minimization, and evaluates them according to the metrics used. The paper also describes the public datasets available for use in the study. In this paper, various segmentation methods are analyzed in detail. Their theoretical and practical characteristics are described for each group of methods, and the most advanced and promising methods are proposed. This paper can help understand the available colorectal segmentation methods, making developing new and more effective and improving existing methods easier. The method is simple to interact with, robust and accurate, and can effectively assist physicians in achieving diagnosis and surgical planning.
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yichao Xie "Colorectal segmentation in medical images", Proc. SPIE 12248, Second International Conference on Sensors and Information Technology (ICSI 2022), 122480U (13 May 2022); https://doi.org/10.1117/12.2637414
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KEYWORDS
Image segmentation

Medical imaging

Image processing algorithms and systems

Tumors

Computed tomography

Colon

Detection and tracking algorithms

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