Abstract
Segmenting brain tumors from multimodal MR scans is thought to be highly beneficial for brain abnormality diagnosis, prognosis monitoring, and treatment evaluation. Due to the highly heterogeneous appearance and shape, segmentation of brain tumors in multimodal MRI scans is a challenging task in medical image analysis. In recent years, many segmentation algorithms based on neural network architecture are proposed to address this task. Observing the previous state-of-the-art algorithms, not only did we explore multimodal brain tumor segmentation in 2D space, 2.5D space and 3D space respectively, we also made a lot of attempts in attention block to improve the segmentation result. In this paper, we describe a 3D deep residual encoder-decoder CNNS with Squeeze-and-Excitation block for brain tumor segmentation. In order to learn more effective image features, we have utilized an attention module after each Res-block to weight each channel, which emphasizes useful features while suppresses invalid ones. To deal with class imbalance, we have formulated a weighted Dice loss function. We find that 3D segmentation network with attention block which can enhance context features can significantly improve the performance. In addition, the results of data preprocessing have a great impact on segmentation performance. Our method obtained Dice scores of 0.70, 0.85 and 0.80 for segmenting enhancing tumor, whole tumor and tumor core, respectively on the testing data set.
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Yan, K., Sun, Q., Li, L., Li, Z. (2020). 3D Deep Residual Encoder-Decoder CNNS with Squeeze-and-Excitation for Brain Tumor Segmentation. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2019. Lecture Notes in Computer Science(), vol 11993. Springer, Cham. https://doi.org/10.1007/978-3-030-46643-5_23
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