Abstract
Automatic segmentation of brain tumors from magnetic resonance imaging (MRI) is a challenging task due to the uneven, irregular and unstructured size and shape of tumors. Recently, brain tumor segmentation methods based on the symmetric U-Net architecture have achieved favorable performance. Meanwhile, the effectiveness of enhancing local responses for feature extraction and restoration has also been shown in recent works, which may encourage the better performance of the brain tumor segmentation problem. Inspired by this, we try to introduce the attention mechanism into the existing U-Net architecture to explore the effects of local important responses on this task. More specifically, we propose an end-to-end 2D brain tumor segmentation network, i.e., attention residual U-Net (AResU-Net), which simultaneously embeds attention mechanism and residual units into U-Net for the further performance improvement of brain tumor segmentation. AResU-Net adds a series of attention units among corresponding down-sampling and up-sampling processes, and it adaptively rescales features to effectively enhance local responses of down-sampling residual features utilized for the feature recovery of the following up-sampling process. We extensively evaluate AResU-Net on two MRI brain tumor segmentation benchmarks of BraTS 2017 and BraTS 2018 datasets. Experiment results illustrate that the proposed AResU-Net outperforms its baselines and achieves comparable performance with typical brain tumor segmentation methods.
Highlights
Brain tumors are abnormal cells growing in human brains, regarded as a type of common neurological disease, which is harmful to human health extremely [1]
We mainly adopt the public BraTS 2017 and BraTS 2018 [4,42] brain tumor segmentation datasets for the performance evaluation. These two datasets are released by the Multimodal Brain Tumor Segmentation Challenge (BraTS) that run in conjunction with the International Conference On Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018
The BraTS 2017 dataset consists of training dataset, validation dataset and test dataset, and each sample has four different modalities, i.e., fluid-attenuated inversion recovery (FLAIR), T1 weighting (T1), T1 weighted contrast enhancement (T1ce), and T2 weighting (T2)
Summary
Brain tumors are abnormal cells growing in human brains, regarded as a type of common neurological disease, which is harmful to human health extremely [1]. As an important way to assist in the diagnosis and treatment of brain tumors, automatic brain tumor segmentation performed on brain magnetic resonance images is of great significance in clinical medicine [2]. Magnetic resonance imaging (MRI) is a typical non-invasive imaging technology, which can produce high-quality brain images without damage and skull artifacts, and is regarded as the main technical means for the diagnosis and treatment of brain tumors. In 2014, Long et al [5] proposed a novel end-to-end fully convolution network (FCN) for natural image segmentation, which injected vitality into the natural image segmentation field [6,7,8,9,10,11,12,13] and was quickly introduced to resolve the brain tumor segmentation problem. A variety of improved U-Net methods, such as ResU-Net [15] and Ensemble Net [16], have been put forward to gain superior performance for the brain tumor segmentation problem
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