论文标题
使用其2D投影执行3D线性结构的连通性
Enforcing connectivity of 3D linear structures using their 2D projections
论文作者
论文摘要
许多生物学和医疗任务都需要描绘出图像体积的3D曲线结构，例如血管和神经突。这通常是使用通过最大程度地减少素养损失函数训练的神经网络来完成的，而素损失函数不会捕获这些结构的拓扑特性。结果，回收结构的连通性通常是错误的，这会减少它们的用处。在本文中，我们建议通过最大程度地减少对2D预测的拓扑感知损失的总和来提高结果的3D连接性。这足以提高准确性并减少提供所需带注释的培训数据所需的注释工作。代码可在https://github.com/doruk-oner/connectivityonprojections上找到。
Many biological and medical tasks require the delineation of 3D curvilinear structures such as blood vessels and neurites from image volumes. This is typically done using neural networks trained by minimizing voxel-wise loss functions that do not capture the topological properties of these structures. As a result, the connectivity of the recovered structures is often wrong, which lessens their usefulness. In this paper, we propose to improve the 3D connectivity of our results by minimizing a sum of topology-aware losses on their 2D projections. This suffices to increase the accuracy and to reduce the annotation effort required to provide the required annotated training data. Code is available at https://github.com/doruk-oner/ConnectivityOnProjections.
