# 3D U-Net for Skull Stripping in Brain MRI
## 1 Introduction  
Brain extraction from a volumetric dataset, T\({}_{1}\)-weighted (T\({}_{1}\)W) magnetic resonance images (MRIs) in general, is called skull stripping [1]. This is important preprocessing and is typically an initial step of most brain MRI studies e.g., cortical surface reconstruction [2], brain volumetric measurement [3], tissue identification [4], brain parts identification [5], multiple sclerosis analysis [6], assessing schizophrenia [7], Alzheimer's disease [8]. Brain MRIs exhibit superior soft tissue contrast that is not normally found in other imaging protocols, such as computed tomography (CT) or X-rays. Automatic skull stripping is a difficult task because of the obscure brain boundaries, low contrast MRIs, and absence of intensity standardization [9]. Moreover, entire brain extraction becomes more challenging when MRI datasets with a pathological disorder are used [10]. After the pioneering results of [PERSON] et al. [11], deep learning, particularly convolutional neural network (CNN)-based algorithms, has become a commonly used algorithmic approach to resolve medical imaging problems and challenges [12]. CNN-based algorithms are trained with known labeled data to learn the underlying mathematical description required for object or region detection, classification, and segmentation [13]. Generally, these algorithms require a vast amount of properly labeled data to train from scratch. However, biomedical image data is usually not sufficient for this challenge. Problems often worsen because labeling data requires a substantial manual effort from a brain anatomy expert in order to accomplish this tedious task [14]. Moreover, manual delineation ofthe brain from MRI is known to alter, even among trained individuals, and be affected by both intra- and inter-rater variabilities. For skull stripping in brain MRIs, manual segmentation of the brain is often considered to be the \"ground truth\" or \"gold standard\" and is frequently employed to validate other semi-automatic and automatic approaches. However, manual skull stripping, although doable, is an extremely time-consuming task. It also requires an urbane knowledge of brain anatomy and is laborious to perform on a large scale. Therefore, it is neither adequate nor efficient. Many approaches considering the problem of skull-stripping in brain MRIs have been proposed over the last two decades and are continually being developed to tackle these problems and limitations. However, every technique has a constraint owing to the enormous variability of brain MRI datasets and standard. These approaches can be broadly divided into two categories: (1) Classical or conventional approaches; and (2) CNN-based or deep learning-based approaches. Classical algorithms can be further classified into the following distinct groups: Thresholding with mathematical morphology [15; 16; 17], deformable surface modal based [18; 19; 20], template or atlas-based [21; 22; 23], and hybrid approaches [9; 24; 25]. A comprehensive survey of all the existing conventional skull-stripping methods can be seen in [26]. Mathematical morphology-based algorithms exploit thresholding, edge detection, and a series of erosion and dilation operations to isolate the skull and the brain region. For instance, [PERSON] et al. [27] proposed a skull-stripping algorithm based on histogram-thresholding followed by morphological operations. [PERSON] and [PERSON] [28] devised an algorithm based on a two-dimensional (2D) region growing for brain T\({}_{1}\)W MRIs. [PERSON] and [PERSON] [29; 30] developed brain extraction algorithm (BEA) for T\({}_{1}\)W and T\({}_{2}\)-weighted (T\({}_{2}\)W) brain MRIs using morphological operations, diffusion, and connected component analysis. [PERSON] et al. devised an algorithm called the Brain Surface Extractor (BSE) [31] that uses anisotropic diffusion filtering, edge detection, and a series of morphological operations to recognize the brain. BSE is extremely fast and generates highly explicit whole brain segmentation.