论文标题
KIPA22报告：U-NET带有轮廓正规化用于肾脏结构的分段
KiPA22 Report: U-Net with Contour Regularization for Renal Structures Segmentation
论文作者
论文摘要
三维（3D）综合肾脏结构（IRS）分割在临床实践中很重要。随着深度学习技术的发展，提出了许多专注于医学图像细分的强大框架。在此挑战中，我们利用了NNU-NET框架，这是医学图像分割的最新方法。为了减少肿瘤标签的异常预测，我们将肿瘤标签的轮廓正则化（CR）丢失与骰子丢失和横向渗透丢失相结合，以改善这种现象。
Three-dimensional (3D) integrated renal structures (IRS) segmentation is important in clinical practice. With the advancement of deep learning techniques, many powerful frameworks focusing on medical image segmentation are proposed. In this challenge, we utilized the nnU-Net framework, which is the state-of-the-art method for medical image segmentation. To reduce the outlier prediction for the tumor label, we combine contour regularization (CR) loss of the tumor label with Dice loss and cross-entropy loss to improve this phenomenon.
