The potential of improving disease detection and treatment planning comes with accurate and fully automatic algorithms for brain tumor segmentation. Glioma, a type of brain tumor, can appear at different locations with different shapes and sizes. Manual segmentation of brain tumor regions is not only time-consuming 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 3D data sets for four MRI modalities (Flair, T1, T1ce, 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 representation. Taking 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, respectively.Experimental results demonstrate that using SegNet models with 3D 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.

中文翻译：

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使用SegNet在多模式MR图像上自动进行脑肿瘤分割

准确而全自动的脑肿瘤分割算法可改善疾病检测和治疗计划的潜力。脑胶质瘤是一种脑肿瘤，可以出现在不同位置，形状和大小不同。手动分割脑肿瘤区域不仅费时，而且容易发生人为错误，其性能取决于病理学家的经验。在本文中，我们通过将完全卷积神经网络SegNet应用于3D数据集的4种MRI模式（Flair，T1，T1ce和T2）来自动分割脑肿瘤和亚肿瘤部位，包括坏死，浮肿和增强肿瘤。为了进一步改善肿瘤分割，四个单独训练的SegNet模型通过后处理进行集成，通过融合每个训练模型的完全卷积层中的机器学习特征图来生成四个最大特征图。组合最大特征图和原始MRI模态的像素强度值，以将有趣的信息编码为特征表示。以组合特征为输入，决策树（DT）用于将MRI体素分类为不同的肿瘤部位和健康的脑组织。在脑肿瘤分割2017（BraTS 2017）挑战提供的数据集上评估提出的算法，我们实现了 组合最大特征图和原始MRI模态的像素强度值，以将有趣的信息编码为特征表示。以组合特征为输入，决策树（DT）用于将MRI体素分类为不同的肿瘤部位和健康的脑组织。在由脑肿瘤分割2017（BraTS 2017）挑战提供的数据集上评估提出的算法，我们实现了 组合最大特征图和原始MRI模态的像素强度值，以将有趣的信息编码为特征表示。以组合特征为输入，决策树（DT）用于将MRI体素分类为不同的肿瘤部位和健康的脑组织。在由脑肿瘤分割2017（BraTS 2017）挑战提供的数据集上评估提出的算法，我们实现了实验结果表明，将SegNet模型与3D MRI数据集结合使用，并将四个最大特征图与原始MRI的像素强度值相结合，整个肿瘤，肿瘤核心和增强性肿瘤的F值分别为0.85、0.81和0.79。方式有可能在脑肿瘤分割方面表现良好。