Author(s)

Xueju Hao ¹

Affiliation(s)

¹ School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, China

Corresponding Author

Xueju Hao

ABSTRACT

Medical image segmentation is a core link in clinical diagnosis, treatment planning, and efficacy evaluation, and its accuracy directly affects the scientificity of medical decisions. With the rapid development of machine vision technology, medical image segmentation based on Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) has become a research hotspot in the field of medical artificial intelligence. This paper focuses on two core scenarios: organ segmentation (e.g., liver, kidney) and lesion segmentation (e.g., tumor), systematically reviewing the basic applications and clinical value of machine vision segmentation technology. First, it combs the development history and core methods of segmentation technology, then introduces the characteristics and application scenarios of classic datasets such as BraTS and LiTS, deeply analyzes key issues currently facing the field including scarcity of annotated data and inconsistent image formats across different hospitals, and discusses the preliminary integration scenarios of technology with clinical diagnosis. Research shows that machine vision segmentation technology can significantly improve the efficiency and accuracy of medical image analysis, providing objective and quantitative reference for clinical practice. However, continuous breakthroughs are still needed in data standardization, model generalization, and clinical adaptability. 

KEYWORDS

Machine Vision; Medical Image Segmentation; CT; MRI; Clinical Auxiliary Diagnosis

CITE THIS PAPER

Xueju Hao, A Review of the Basic Applications of Machine Vision in Medical Image Segmentation. Journal of Artificial Intelligence Practice (2025) Vol. 8: 152-159. DOI: http://dx.doi.org/10.23977/jaip.2025.080319.

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