论文标题
CT中的CNN图像分割：beyound损失功能，用于实现地面真相图像
CNN in CT Image Segmentation: Beyound Loss Function for Expoliting Ground Truth Images
论文作者
论文摘要
现在利用地面真相（GT）图像的更多信息是进一步改善CNN在CT图像分段中的性能的新研究方向。以前的方法着重于设计损失函数以实现此类目的。但是，设计一般且优化的损失功能很难。我们在这里提出了一种新颖而实用的方法，该方法利用了超出损失函数的GT图像。我们的见解是，在GT和CT图像上分别训练了两个CNN的特征地图在某些度量空间上应该相似，因为它们都用于以相同的目的来描述相同的对象。因此，我们通过强制执行这两个CNN的特征图以保持一致来利用GT图像。我们在两个数据集上评估了提出的方法，并将其性能与几种竞争方法进行比较。广泛的实验结果表明，所提出的方法是有效的，表现优于所有比较方法。
Exploiting more information from ground truth (GT) images now is a new research direction for further improving CNN's performance in CT image segmentation. Previous methods focus on devising the loss function for fulfilling such a purpose. However, it is rather difficult to devise a general and optimization-friendly loss function. We here present a novel and practical method that exploits GT images beyond the loss function. Our insight is that feature maps of two CNNs trained respectively on GT and CT images should be similar on some metric space, because they both are used to describe the same objects for the same purpose. We hence exploit GT images by enforcing such two CNNs' feature maps to be consistent. We assess the proposed method on two data sets, and compare its performance to several competitive methods. Extensive experimental results show that the proposed method is effective, outperforming all the compared methods.
