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首页> 外文期刊>Medical image analysis >Fully automatic brain tumor segmentation with deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy
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Fully automatic brain tumor segmentation with deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy

机译：使用重叠贴片和多级加权交叉熵进行完全自动脑肿瘤分割，基于深度学习的选择性关注

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摘要

In this paper, we present a new Deep Convolutional Neural Networks (CNNs) dedicated to fully automatic segmentation of Glioblastoma brain tumors with high- and low-grade. The proposed CNNs model is inspired by the Occipito-Temporal pathway which has a special function called selective attention that uses different receptive field sizes in successive layers to figure out the crucial objects in a scene. Thus, using selective attention technique to develop the CNNs model, helps to maximize the extraction of relevant features from MRI images. We have also addressed two more issues: class-imbalance, and the spatial relationship among image Patches. To address the first issue, we propose two steps: an equal sampling of images Patches and an experimental analysis of the effect of weighted cross-entropy loss function on the segmentation results. In addition, to overcome the second issue, we have studied the effect of Overlapping Patches against Adjacent Patches where the Overlapping Patches show better segmentation results due to the introduction of the global context as well as the local features of the image Patches compared to the conventionnel Adjacent Patches. Our experiment results are reported on BRATS-2018 dataset where our End-to-End Deep Learning model achieved state-of-the-art performance. The median Dice score of our fully automatic segmentation model is 0.90, 0.83, 0.83 for the whole tumor, tumor core, and enhancing tumor respectively compared to the Dice score of radiologist, that is in the range 74%-85%. Moreover, our proposed CNNs model is not only computationally efficient at inference time, but it could segment the whole brain on average 12 seconds. Finally, the proposed Deep Learning model provides an accurate and reliable segmentation result, and that makes it suitable for adopting in research and as a part of different clinical settings. (C) 2020 Elsevier B.V. All rights reserved.
机译：在本文中，我们提出了一种新的深度卷积神经网络（CNNS），致力于全自动地分割胶质母细胞瘤脑肿瘤，具有高和低等级。所提出的CNNS模型由枕颞途径的启发，该幂途径具有特殊功能，称为选择性注意，其在连续的层中使用不同的接收场尺寸来弄清一些场景中的关键物体。因此，使用选择性注意技术来开发CNNS模型，有助于最大化来自MRI图像的相关特征的提取。我们还解决了两个问题：类别不平衡，以及图像补丁之间的空间关系。要解决第一个问题，我们提出了两个步骤：相同的图像补丁采样和对分段结果对加权交叉熵损失功能的实验分析。此外，为了克服第二个问题，我们已经研究了重叠贴片对相邻斑块的影响，其中重叠的补丁在与旁路相比的图像贴片的局部特征以及图像贴片的本地特征引起的邻近的斑块相邻的补丁。我们的实验结果据报道，在Brats-2018数据集上，我们的端到端深度学习模式实现了最先进的性能。与放射科学家的骰子评分相比，我们全自动分割模型的中位数分数为0.90,0.83,0.83分别为整体肿瘤，肿瘤核心和增强肿瘤，其范围为74％-85％。此外，我们所提出的CNNS模型不仅在推理时间计算上有效，而且它可以平均为整个大脑进行12秒。最后，建议的深度学习模型提供了准确可靠的分割结果，这使得适用于采用研究以及作为不同临床环境的一部分。 （c）2020 Elsevier B.V.保留所有权利。

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