applied sciences  
A Fully Automatic Procedure for Brain Tumor Segmentation from Multi-Spectral MRI Records Using Ensemble Learning and Atlas-Based Data Enhancement  
[PERSON] 122, [PERSON] 123*  and [PERSON] 133 13  
This publication is supported by the National Natural Science Foundation of China under grant number 10  
invasive than other imaging modalities, like positron emission tomography (PET) or computed tomography (CT). With its high contrast and good resolution, it can provide accurate data about the structure of the tumor. Multi-modal or multi-spectral MRI, through its T1-weighted (with or without contrast improvement) and T2-weighted (with or without fluid attenuated inversion recovery) data channels, can significantly contribute to the better visibility of intracranial structures [3]. The quick development of computerized medical devices and the economic rise of several underdeveloped countries both contribute to the fast spreading of MRI equipment in hospitals worldwide. These MRI devices produce more and more image data. Training enough human experts to process these records would be very costly, if possible at all. This is why there is a strong need for automatic algorithms that can reliably process the acquired image data and select those records that need to be inspected by the human experts, who have the final word in establishing the diagnosis. Although such algorithms are never perfect, they may contribute to cost reduction and allow for screening large masses of the population, leading to several early detected tumors. The brain tumor segmentation problem is a difficult task in medical image processing, having major obstacles like: (1) the large variety of the locations, shapes, and appearances of the tumor; (2) displacement and distortion of normal tissues caused by the focal lesion; (3) the variety of imaging modalities and weighting schemes applied in MRI, which provide different types of biological information; (4) multi-channel data are not perfectly registered together; (5) numerical values produced by MRI do not directly reflect the observed tissues; they need to be interpreted in their context; (6) intensity inhomogeneity may be present in MRI measurements due to the turbulence of the magnetic field. The history of automatic brain tumor segmentation from MRI records can be divided into two eras: the pre-BraTS and the BraTS era, where BraTS refers to the Brain Tumor Segmentation Challenges [3; 4] organized every year since 2012 by the MICCAI society, which had an enormous impact with the introduction of a multi-spectral MRI data set that can be used as a standard in the evaluation of segmentation methods. Earlier solutions were usually developed for single data channels and even for 2D data (or reduced number of distant slices) and mostly validated with private collections of MRI records that do not allow for objective comparison. A remarkable review on early segmentation methods, including manual, semi-automatic, and fully automatic solutions, was provided by [PERSON] et al. [5]. Methods developed in the BraTS era are usually fully automatic and employ either one or a combination of: (1) advanced general-purpose image segmentation techniques (mostly unsupervised); (2) classical machine learning algorithms (both supervised and unsupervised); or (3) deep learning convolutional neural networks (supervised learning methods). All methods developed and published in the pre-BraTS era belong to the first two groups or their combination. They dominate the first years of the BraTS era as well, and they are still holding significant research interest. Unsupervised methods have the advantages that they do not require large amounts of training data and provide segmentation results in a relatively short time. They organize the input data into several clusters, each consisting of highly similar data. However, they either have difficulty with correctly labeling the clusters or they require manual interaction.