论文标题
相邻的切片具有指导性的2.5D网络，用于肺结节分割
Adjacent Slice Feature Guided 2.5D Network for Pulmonary Nodule Segmentation
论文作者
论文摘要
越来越多的关注肺结节分割。在基于深度学习的当前方法中，3D分割方法直接输入3D图像，该方法占据了很多内存并带来了巨大的计算。但是，大多数参数较少和计算的2D分割方法都存在切片之间缺乏空间关系的问题，导致分割性能差。为了解决这些问题，我们提出了一个相邻的切片功能指导2.5D网络。在本文中，我们设计了一个相邻的切片特征融合模型，以介绍来自相邻切片的信息。为了进一步提高模型性能，我们构建了一个多尺度融合模块以捕获更多上下文信息，此外，我们设计了一个边缘约束损耗函数，以优化边缘区域中的分割结果。充分的实验表明，我们的方法在肺结节分割任务中的执行比其他现有方法更好。
More and more attention has been paid to the segmentation of pulmonary nodules. Among the current methods based on deep learning, 3D segmentation methods directly input 3D images, which takes up a lot of memory and brings huge computation. However, most of the 2D segmentation methods with less parameters and calculation have the problem of lacking spatial relations between slices, resulting in poor segmentation performance. In order to solve these problems, we propose an adjacent slice feature guided 2.5D network. In this paper, we design an adjacent slice feature fusion model to introduce information from adjacent slices. To further improve the model performance, we construct a multi-scale fusion module to capture more context information, in addition, we design an edge-constrained loss function to optimize the segmentation results in the edge region. Fully experiments show that our method performs better than other existing methods in pulmonary nodule segmentation task.
