JOURNAL ARTICLE

Medical image segmentation methods overview

Bohdan ChapaliukYuriy Zaychenko

Year: 2018 Journal:   System research and information technologies Vol: 0 (1)Pages: 72-81   Publisher: Igor Sikorsky Kyiv Polytechnic Institute

Abstract

This article provides an overview of the modern medical image segmentation methods. The most popular methods such as multi-atlas based methods and deep learning approach are considered in more details. In addition, this article overviews different steps of the multi-atlas based methods (MAS) in detail and shows which modern algorithms and approaches used in different steps of MAS to achieve state of the art results in the medical image segmentation task and how it affects the accuracy of the algorithm. Also, there is a brief description of the modern deep learning algorithms which are used for the medical image segmentation. Such type of algorithm is used as an independent algorithm or as a part of the MAS. Finally, this article summarizes described algorithms and evaluates which approaches promise to improve state of the art result of the medical image segmentation in the future.

Keywords:
Computer science Segmentation Artificial intelligence Image segmentation Segmentation-based object categorization Deep learning Scale-space segmentation Image (mathematics) Task (project management) Atlas (anatomy) Computer vision Pattern recognition (psychology) Engineering Medicine

Metrics

1
Cited By
0.00
FWCI (Field Weighted Citation Impact)
36
Refs
0.05
Citation Normalized Percentile
Is in top 1%
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Citation History

Topics

Medical Image Segmentation Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
AI in cancer detection
Physical Sciences →  Computer Science →  Artificial Intelligence
Brain Tumor Detection and Classification
Life Sciences →  Neuroscience →  Neurology

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