MRI Segmentation of the Human Brain: Challenges, Methods, and Applications

Abstract

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.

Vascular Extraction Using MRA Statistics and Gradient Information

“…As reviewed in [2], various segmentation techniques have been introduced in the literature. A complete review of existing methods for cerebral vessel segmentation can be referenced at [3].…”

Multi-atlas label fusion by using supervised local weighting for brain image segmentation

“…In practice, such alignment is deteriorated when structures largely vary [16]. Although non-rigid transformations have been proposed to cope with this issue, registration is still very hard to perform in the presence of large structure deformations, as in the case of brain lesions or neurodegenerative disease [17].…”

The impact of correction on MP2RAGE cortical T1 and apparent cortical thickness at 7T

“…In particular, inhomogeneous radiofrequency (RF) B 1 transmit and receive profiles lead to signal intensity variations (i.e., ${\mathrm{normalB}}_1^{+}$ and ${\mathrm{normalB}}_1^{-}$ bias fields, respectively) that hamper accurate classification of WM, GM, and CSF and cortical GM thickness estimates (Collins, Liu, Schreiber, Yang, & Smith, 2005; De Martino et al, 2015; Lorio et al, 2016; Van de Moortele et al, 2005). Especially for submillimeter acquisitions, laborious manual work is required to correct errors of the automatic segmentation, potentially introducing observer‐dependent errors and biases (Despotovic, Goossens, & Philips, 2015; Fischl et al, 2004; Gulban, Schneider, Marquardt, Haast, & De Martino, 2018; Polimeni, Renvall, Zaretskaya, & Fischl, 2017). The most severe artefacts are observed toward the inferior temporal and frontal lobe regions, preventing even their manual segmentation.…”