论文标题
RSANET：多发性硬化病变细分的经常性切片注意网络
RSANet: Recurrent Slice-wise Attention Network for Multiple Sclerosis Lesion Segmentation
论文作者
论文摘要
在T2加权MRI图像上测得的脑病变体积是多发性硬化症（MS）中临床上重要的疾病标志物。 MS病变的手动描述是一项耗时且高度依赖操作员的任务，受病变大小，形状和明显的影响。最近，已经开发了基于深神经网络的自动病变分割算法，结果有令人鼓舞的结果。在本文中，我们提出了一种新颖的经常性切片注意网络（RSANET），该网络将3D MRI图像建模为切片序列，并通过经常性的方式捕获长期依赖性，以利用MS病变的上下文信息。具有43名患者的数据集上的实验表明，该方法的表现优于最先进的方法。我们的实施可在线访问https://github.com/tinymilky/rsanet。
Brain lesion volume measured on T2 weighted MRI images is a clinically important disease marker in multiple sclerosis (MS). Manual delineation of MS lesions is a time-consuming and highly operator-dependent task, which is influenced by lesion size, shape and conspicuity. Recently, automated lesion segmentation algorithms based on deep neural networks have been developed with promising results. In this paper, we propose a novel recurrent slice-wise attention network (RSANet), which models 3D MRI images as sequences of slices and captures long-range dependencies through a recurrent manner to utilize contextual information of MS lesions. Experiments on a dataset with 43 patients show that the proposed method outperforms the state-of-the-art approaches. Our implementation is available online at https://github.com/tinymilky/RSANet.
