论文标题
肾脏癌治疗的多结构分割的结合自动编码器正规化
Ensembled Autoencoder Regularization for Multi-Structure Segmentation for Kidney Cancer Treatment
论文作者
论文摘要
肾癌是最常见的癌症类型之一。治疗经常包括手术干预。但是，在这种情况下，由于区域解剖学关系，手术尤其具有挑战性。器官描述可以显着改善手术计划和执行。在这项贡献中，我们提出了两个完全卷积网络的合奏，以分割肾脏，肿瘤，静脉和动脉。尽管Segresnet架构在肿瘤上取得了更好的性能，但NNU-NET为肾脏，动脉和静脉提供了更精确的分割。因此，在我们提出的方法中，我们将这两个网络结合在一起，并通过增加混合增强来进一步提高性能。
The kidney cancer is one of the most common cancer types. The treatment frequently include surgical intervention. However, surgery is in this case particularly challenging due to regional anatomical relations. Organ delineation can significantly improve surgical planning and execution. In this contribution, we propose ensemble of two fully convolutional networks for segmentation of kidney, tumor, veins and arteries. While SegResNet architecture achieved better performance on tumor, the nnU-Net provided more precise segmentation for kidneys, arteries and veins. So in our proposed approach we combine these two networks, and further boost the performance by mixup augmentation.
