论文标题
深度多发性硬化病变细分的几何损失
Geometric Loss for Deep Multiple Sclerosis lesion Segmentation
论文作者
论文摘要
多发性硬化症（MS）病变占大脑体积的一小部分，并且在形状，大小和位置方面是异质的，这对训练基于深度学习的分割模型构成了巨大挑战。我们提出了一个新的几何损失公式，以解决数据失衡并利用MS病变的几何特性。我们表明，传统的基于区域和边界感知的损失函数可以与公式相关联。我们进一步开发并实例化了两个损失函数，其中包含病变区域的一阶几何信息，以在优化深层分割模型时实施正则化。与其他最新方法相比，对两个具有不同尺度，采集方案和决议的MS病变数据集的实验结果证明了我们所提出方法的优越性。
Multiple sclerosis (MS) lesions occupy a small fraction of the brain volume, and are heterogeneous with regards to shape, size and locations, which poses a great challenge for training deep learning based segmentation models. We proposed a new geometric loss formula to address the data imbalance and exploit the geometric property of MS lesions. We showed that traditional region-based and boundary-aware loss functions can be associated with the formula. We further develop and instantiate two loss functions containing first- and second-order geometric information of lesion regions to enforce regularization on optimizing deep segmentation models. Experimental results on two MS lesion datasets with different scales, acquisition protocols and resolutions demonstrated the superiority of our proposed methods compared to other state-of-the-art methods.
