论文标题
使用基于注意的CNN中的多发性硬化病变分割
Multiple Sclerosis Lesions Segmentation using Attention-Based CNNs in FLAIR Images
论文作者
论文摘要
目的：多发性硬化症（MS）是一种自身免疫性和脱髓鞘性疾病，导致中枢神经系统病变。可以使用磁共振成像（MRI）跟踪和诊断该疾病。到目前为止，多种多模式自动生物医学方法用于细分对患者的成本，时间和可用性对患者无益的病变。本文的作者提出了一种仅采用一种模态（FLAIR图像）来准确细分MS病变的方法。方法：基于斑块的卷积神经网络（CNN）的设计，灵感来自3D-RESNET和空间通道注意模块，以分段MS病变。所提出的方法由三个阶段组成：（1）将对比度限制的自适应直方图均衡（CLAHE）应用于原始图像，并与提取的边缘串联以创建4D图像； （2）大小80 * 80 * 80 * 2的贴片是从4D图像中随机选择的； （3）提取的斑块被传递到基于注意力的CNN中，用于细分病变。最后，将提出的方法与以前对同一数据集的研究进行了比较。结果：当前的研究通过ISIB挑战数据进行了测试集评估模型。实验结果表明，所提出的方法在骰子相似性和绝对体积差方面显着超过了现有的方法，而所提出的方法仅使用一种模态（FLAIR）来分割病变。结论：作者引入了一种自动方法，以分割病变，最多基于两种方式作为输入。所提出的结构由卷积，反卷积和SCA-Voxres模块作为注意模块组成。结果表明，所提出的方法优于其他方法。
Objective: Multiple Sclerosis (MS) is an autoimmune, and demyelinating disease that leads to lesions in the central nervous system. This disease can be tracked and diagnosed using Magnetic Resonance Imaging (MRI). Up to now a multitude of multimodality automatic biomedical approaches is used to segment lesions which are not beneficial for patients in terms of cost, time, and usability. The authors of the present paper propose a method employing just one modality (FLAIR image) to segment MS lesions accurately. Methods: A patch-based Convolutional Neural Network (CNN) is designed, inspired by 3D-ResNet and spatial-channel attention module, to segment MS lesions. The proposed method consists of three stages: (1) the contrast-limited adaptive histogram equalization (CLAHE) is applied to the original images and concatenated to the extracted edges in order to create 4D images; (2) the patches of size 80 * 80 * 80 * 2 are randomly selected from the 4D images; and (3) the extracted patches are passed into an attention-based CNN which is used to segment the lesions. Finally, the proposed method was compared to previous studies of the same dataset. Results: The current study evaluates the model, with a test set of ISIB challenge data. Experimental results illustrate that the proposed approach significantly surpasses existing methods in terms of Dice similarity and Absolute Volume Difference while the proposed method use just one modality (FLAIR) to segment the lesions. Conclusions: The authors have introduced an automated approach to segment the lesions which is based on, at most, two modalities as an input. The proposed architecture is composed of convolution, deconvolution, and an SCA-VoxRes module as an attention module. The results show, the proposed method outperforms well compare to other methods.
