论文标题
技术报告：使用2D U-NET进行肾脏肿瘤分割，然后进行统计后处理过滤器
Technical report: Kidney tumor segmentation using a 2D U-Net followed by a statistical post-processing filter
论文作者
论文摘要
每年，全世界大约有400000例肾癌新病例造成约17.5万例死亡。对于临床决策，了解肿瘤的形态计量学很重要，这涉及在3D CT图像中描述肿瘤和肾脏的耗时任务。自动分割可能是临床医生和研究人员还研究肿瘤形态和临床结局之间相关性的重要工具。我们提出了一种细分方法，该方法将流行的U-NET卷积神经网络体系结构与基于可用培训数据的统计约束的后处理结合在一起。基于Pytorch和受过训练的权重的完整实现可以在Github上找到。
Each year, there are about 400'000 new cases of kidney cancer worldwide causing around 175'000 deaths. For clinical decision making it is important to understand the morphometry of the tumor, which involves the time-consuming task of delineating tumor and kidney in 3D CT images. Automatic segmentation could be an important tool for clinicians and researchers to also study the correlations between tumor morphometry and clinical outcomes. We present a segmentation method which combines the popular U-Net convolutional neural network architecture with post-processing based on statistical constraints of the available training data. The full implementation, based on PyTorch, and the trained weights can be found on GitHub.
