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Automated brain tumor segmentation on multi-modal MR image using SegNet

Computational Visual Media 2019年第2期5卷 209-219页

作者： Salma Alqazzaz Xianfang Sun Xin Yang len nokes School of Engineering Cardiff UniversityCardiffCF24 3AAUK Department of Physics College of Science for WomenBaghdad UniversityBaghdadIraq School of Computer Science and Informatics Cardiff UniversityCardiffCF24 3AAUK

The potential of improving disease detection and treatment planning comes with accurate and fully automatic algorithms for brain tumor ***, a type of brain tumor, can appear at different locations with different shapes and sizes. Manual segmentation of brain tumor regions is not only timeconsuming but also prone to human error, and its performance depends on pathologists’ experience. In this paper, we tackle this problem by applying a fully convolutional neural network SegNet to 3 D data sets for four MRI modalities(Flair, T1, T1 ce, and T2)for automated segmentation of brain tumor and subtumor parts, including necrosis, edema, and enhancing tumor. To further improve tumor segmentation, the four separately trained SegNet models are integrated by post-processing to produce four maximum feature maps by fusing the machine-learned feature maps from the fully convolutional layers of each trained model. The maximum feature maps and the pixel intensity values of the original MRI modalities are combined to encode interesting information into a feature *** the combined feature as input, a decision tree(DT) is used to classify the MRI voxels into different tumor parts and healthy brain tissue. Evaluating the proposed algorithm on the dataset provided by the Brain Tumor Segmentation 2017(BraTS 2017)challenge, we achieved F-measure scores of 0.85, 0.81,and 0.79 for whole tumor, tumor core, and enhancing tumor, *** results demonstrate that using SegNet models with 3 D MRI datasets and integrating the four maximum feature maps with pixel intensity values of the original MRI modalities has potential to perform well on brain tumor segmentation.

关键词： brain tumor segmentation multi-modal MRI convolutional neural networks fully convolutional networks decision tree

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