HTTU-Net: Hybrid Two Track U-Net for Automatic Brain Tumor Segmentation
HTTU-Net: Hybrid Two Track U-Net for Automatic Brain Tumor Segmentation
复制标题
DOI：
10.1109/access.2020.2998601
复制
发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Afifi, Ahmed
中科院分区：
文献类型：
--
作者：
Aboelenein, Nagwa M.;Piao Songhao;Afifi, Ahmed
脑癌是导致癌症死亡的最主要原因之一；诊断和治疗脑瘤的最好方法是早期筛查。磁共振成像(MRI)通常用于脑肿瘤的诊断，然而，如何获得更高的准确率和性能是一个具有挑战性的问题，这是以前提出的大多数自动化医疗诊断中的一个关键问题。本文提出了一种混合双轨U-Net(HTTU-Net)结构用于脑肿瘤分割。该体系结构利用了Leaky Relu激活和批处理标准化的使用。它包括两个轨道；每个轨道具有不同数量的层，并使用不同的内核大小。然后，我们将这两个轨迹合并以生成最终的分割。我们使用焦损和广义骰子(GDL)损耗函数来解决类不平衡问题。在Brats 2018年的数据集上对所提出的分割方法进行了评估，获得了整个肿瘤区域的平均骰子相似系数为0.865，核心区为0.808，增强区域为0.745，整个肿瘤、核心区域和增强区域的平均骰子相似系数分别为0.883，0.895和0.815。提出的HTTU-Net结构对于脑肿瘤的分割是足够的，并且获得了高精度的结果。本文还对其他定量和定性评价进行了讨论。它证实了我们的结果是非常可比的专家人类水平的表现，并可以帮助专家减少诊断时间。
Brain cancer is one of the most dominant causes of cancer death; the best way to diagnose and treat brain tumors is to screen early. Magnetic Resonance Imaging (MRI) is commonly used for brain tumor diagnosis; however, it is a challenging problem to achieve higher accuracy and performance, which is a vital problem in most of the previously presented automated medical diagnosis. In this paper, we propose a Hybrid Two-Track U-Net(HTTU-Net) architecture for brain tumor segmentation. This architecture leverages the use of Leaky Relu activation and batch normalization. It includes two tracks; each one has a different number of layers and utilizes a different kernel size. Then, we merge these two tracks to generate the final segmentation. We use the focal loss, and generalized Dice (GDL), loss functions to address the problem of class imbalance. The proposed segmentation method was evaluated on the BraTS'2018 datasets and obtained a mean Dice similarity coefficient of 0.865 for the whole tumor region, 0.808 for the core region and 0.745 for the enhancement region and a median Dice similarity coefficient of 0.883, 0.895, and 0.815 for the whole tumor, core and enhancing region, respectively. The proposed HTTU-Net architecture is sufficient for the segmentation of brain tumors and achieves highly accurate results. Other quantitative and qualitative evaluations are discussed, along with the paper. It confirms that our results are very comparable expert human-level performance and could help experts to decrease the time of diagnostic.