点击次数：

DOI码：10.1016/j.ymeth.2022.04.001

发表刊物：Method

摘要：In medical and material science, 3D reconstruction is of great importance for quantitative analysis of microstructures. After the image segmentation process of serial slices, in order to construct each local structure in volume data, it needs to use precise object tracking algorithm to recognize the same object region in adjacent slice. Suffering from weak representative hand-crafted features, traditional object tracking methods always draw out under-segmentation results. In this work, we have proposed an adjacent similarity based deep learning tracking method (ASDLTrack) to reconstruct 3D microstructure. By transferring object tracking problem to classification problem, it can utilize powerful representative ability of convolutional neural network in pattern recognition. Experiments in three datasets with three metrics demonstrate that our algorithm achieves the promising performance compared to traditional methods.

合写作者：Yuting Xu,Jiahao Chen,Pan Puquan,Xiaojuan Ban,Hao Wang,Weihua Xue

第一作者：Boyuan Ma

论文类型：期刊论文

学科门类：工学

文献类型：J

期号：204

页面范围：172-178

是否译文：否

发表时间：2022-01-01

收录刊物：SCI