Abstract
Brain tumor localization and segmentation from magnetic resonance imaging (MRI) are hard and important tasks for several applications in the field of medical analysis. As each brain imaging modality gives unique and key details related to each part of the tumor, many recent approaches used four modalities T1, T1c, T2, and FLAIR. Although many of them obtained a promising segmentation result on the BRATS 2018 dataset, they suffer from a complex structure that needs more time to train and test. So, in this paper, to obtain a flexible and effective brain tumor segmentation system, first, we propose a preprocessing approach to work only on a small part of the image rather than the whole part of the image. This method leads to a decrease in computing time and overcomes the overfitting problems in a Cascade Deep Learning model. In the second step, as we are dealing with a smaller part of brain images in each slice, a simple and efficient Cascade Convolutional Neural Network (C-ConvNet/C-CNN) is proposed. This C-CNN model mines both local and global features in two different routes. Also, to improve the brain tumor segmentation accuracy compared with the state-of-the-art models, a novel Distance-Wise Attention (DWA) mechanism is introduced. The DWA mechanism considers the effect of the center location of the tumor and the brain inside the model. Comprehensive experiments are conducted on the BRATS 2018 dataset and show that the proposed model obtains competitive results: the proposed method achieves a mean whole tumor, enhancing tumor, and tumor core dice scores of 0.9203, 0.9113 and 0.8726 respectively. Other quantitative and qualitative assessments are presented and discussed.
Highlights
Brain tumors include the most threatening types of tumors around the world
As an initial step in this kind of segmentation, the key information is extracted from the input image using some feature extraction algorithm, and a discriminative model is trained to recognize the tumor from normal tissues
We have developed a new brain tumor segmentation architecture that benefits from the characterization of the four magnetic resonance imaging (MRI) modalities
Summary
Brain tumors include the most threatening types of tumors around the world. Glioma, the most common primary brain tumors, occurs due to the carcinogenesis of glial cells in the spinal cord and brain. As an initial step in this kind of segmentation, the key information is extracted from the input image using some feature extraction algorithm, and a discriminative model is trained to recognize the tumor from normal tissues. We do not use a complex deep learning model to define the location of the tumor and extract features that lead to a time-consuming process with a high fault rate.
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