# 3D U-Net for Skull Stripping in Brain MRI
## 1 Introduction
The main drawback of this technique is that it generally requires parameter tuning to work on a particular dataset. In summary, morphological operations are highly dependent on the size and shape of the structuring element that directly influences the brain extraction results of these algorithms. The brain extraction tool (BET) [32] is the most prevalent algorithm based on deformable surface modal-based methods. It applies locally adopted model forces to fit the surface of the brain employing a deformable model. The BET failed to segment the brain region in the inferior axial slices (slices with neck) when the center of gravity of the volume lie outside the brain. [PERSON] et al. [33] proposed a model-based level set (MLS) algorithm to separate intracranial tissues and skull encircling the brain. These techniques fail to segment the brain in noisy and low-contrast MRI datasets. Generally, deformable surface modal-based methods have the potential to yield more accurate and precise skull stripping in brain MRI than approaches using mathematical morphology, edge, and thresholding. Template or atlas-based approaches depend on a fitting template or atlas on the brain MRIs to isolate them from non-brain tissues. These methods can separate the brain from the skull when there is no well-defined relationship between the brain region and pixel intensity present in the brain MRIs. [PERSON] et al. [21] described a brain extraction step as a preprocessing in a cortical surface reconstruction process using a tessellated ellipsoidal template. [PERSON] et al. [23] presented a method called \"Brain MAPS\" that produces a brain segmentation utilizing a template library and atlas registration. Similarly, [PERSON] et al. [34] developed BEAST, a brain extraction method, based on nonlocal segmentation and a multi-resolution framework. Other recent algorithms include those mentioned in [35; 36]. The accuracy of template or atlas-based approaches depends on the quality of brain mask and registration in each atlas. Furthermore, these methods are generally computationally intensive. Hybrid approaches combine the advantages of two or more methods to increase the accuracy and precision of skull-stripping in brain MRIs. ROBEX (Robust Brain Extraction) [9] is a well-known learning-based algorithm that incorporates a discriminative (random forest) and a generative model (point distribution) with the graph cuts for skull stripping. Unfortunately, hybrid approaches often involve extensive training to learn the peculiar brain features to accurately skull strip the brain MRIs. Therefore, brain extraction remains a rate-limiting and tedious step in the brain MRIs analysis pipeline. Recently, CNNs and deep learning-based techniques have achieved excellent performance in biomedical 2D image segmentation, with an accuracy close to human performance [37; 38; 39]. After successful application of CNNs on 2D biomedical images, efforts had been made on 3D biomedical volumetric data, specifically on the brain MRI [10; 40]. Auto-Context CNN (Auto-Net) [41], Active Shape Model and CNN (ASM-CNN) [42], and complementary segmentation networks (CompNet) [43] are the most recently published works on brain segmentation. [PERSON] et al. [40] presented CNN with Hough voting approach that enables fully automatic localization and segmentation of a region of interest for 3D segmentation of the deep brain region. [PERSON] et al. [10] firstly proposed an end-to-end approach for brain extraction and skull stripping based on 3D CNN and achieved good performance. Their architecture can handle many forms including contrast-enhanced scans. Although [PERSON]'s method is the first CNN for the problem and showed state-of-the-art performance, the network is not deep. Note that the depth of network is not a problem for this specific task at hand, but it can be a problem when generalizing this network for other tasks. Generally, the shallow depth has a limited learning capability because shallow layers cannot integrate features from various levels of abstraction.