A Review on Typical and Modern Brain MRI Image Segmentation Methods and Challenges
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Abstract
Background: Brain image segmentation is one of the essential tasks in medical image analysis. Digital Brain MR Images usually contain Noise, inhomogeneity, and sometimes deviation due to the capturing device's configuration. Therefore, accurate segmentation of brain MRI images is deployed to measure and visualize the brain's anatomical structures, analyze brain changes, delineate pathological regions, and for surgical planning and image-guided interventions. During the past few decades, various segmentation techniques of different accuracy and degree of complexity have been developed and reported in the literature. In this paper, several popular methods are used for brain MRI segmentation and focus on their capabilities, advantages, and pitfalls. Likewise, we also discuss modern image segmentation techniques by Deep Learning Technology and deliberate the metrics to evaluate the brain tumor segmentation and dataset availability performance. Eventually, we suggest future research challenges among brain tumor multimodal imaging techniques.
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