MRI Segmentation of the Human Brain: Challenges, Methods, and Applications
By Ivana Despotovic, Bart Goossens, Wilfried Philips. Comput. Math. Methods Medicine, 2015
687 citations recorded by the source corpus.
Image segmentation is one of the most important tasks in medical image analysis and is often the first and the most critical step in many clinical applications. In brain MRI analysis, image segmentation is commonly used for measuring and visualizing the brain's anatomical structures, for analyzing brain changes, for delineating pathological regions, and for surgical planning and image-guided interventions. In the last few decades, various segmentation techniques of different accuracy and degree of complexity have been developed and reported in the literature. In this paper we review the most popular methods commonly used for brain MRI segmentation. We highlight differences between them and discuss their capabilities, advantages, and limitations. To address the complexity and challenges of the brain MRI segmentation problem, we first introduce the basic concepts of image segmentation. Then, we explain different MRI preprocessing steps including image registration, bias field correction, and removal of nonbrain tissue. Finally, after reviewing different brain MRI segmentation methods, we discuss the validation problem in brain MRI segmentation.
Subfields: Computer Vision and Pattern Recognition, Neurology
View Paper: https://www.semanticscholar.org/paper/6aeb17f02edf4b6ebc72549e34dba8aadbe8bf19
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In Computer Vision and Pattern Recognition
Citations in the corpus
- VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images (2017, positive): “In addition, the imaging acquisition protocol can affect the imaging quality significantly, which further poses challenges for automated segmentation methods (Despotović et al., 2015).”