标题： 用于肾肿瘤分割的端到端级联U-Net与定位网络 显示英文标题

标题： End-to-End Cascaded U-Nets with a Localization Network for Kidney Tumor Segmentation

摘要： 肾肿瘤分割成为医学影像领域计算机视觉的新前沿，这在一定程度上是因为其具有挑战性的手动标注以及重大的医学影响。在2019年肾肿瘤分割挑战赛的范围内，该挑战旨在实现肾脏和肿瘤的同时分割，本文提出了一种新颖的三维U-Net组合方法——统称为TuNet——利用生成的肾脏掩膜进行连续的肿瘤分割。所提出的方法在数据集中210名患者的基础上，通过五折交叉验证，实现了肾脏分割的S{\o }ensen-Dice系数为0.902，肿瘤分割的系数为0.408。 显示英文摘要

摘要： Kidney tumor segmentation emerges as a new frontier of computer vision in medical imaging. This is partly due to its challenging manual annotation and great medical impact. Within the scope of the Kidney Tumor Segmentation Challenge 2019, that is aiming at combined kidney and tumor segmentation, this work proposes a novel combination of 3D U-Nets---collectively denoted TuNet---utilizing the resulting kidney masks for the consecutive tumor segmentation. The proposed method achieves a S{\o}rensen-Dice coefficient score of 0.902 for the kidney, and 0.408 for the tumor segmentation, computed from a five-fold cross-validation on the 210 patients available in the data.