Computer Vision Laboratory, Faculté Informatique et Communications, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
Neuroinformatics. 2011 Sep;9(2-3):279-302. doi: 10.1007/s12021-011-9122-1.
We present a novel probabilistic approach to fully automated delineation of tree structures in noisy 2D images and 3D image stacks. Unlike earlier methods that rely mostly on local evidence, ours builds a set of candidate trees over many different subsets of points likely to belong to the optimal tree and then chooses the best one according to a global objective function that combines image evidence with geometric priors. Since the best tree does not necessarily span all the points, the algorithm is able to eliminate false detections while retaining the correct tree topology. Manually annotated brightfield micrographs, retinal scans and the DIADEM challenge datasets are used to evaluate the performance of our method. We used the DIADEM metric to quantitatively evaluate the topological accuracy of the reconstructions and showed that the use of the geometric regularization yields a substantial improvement.
我们提出了一种新颖的概率方法，用于全自动勾画噪声 2D 图像和 3D 图像堆栈中的树状结构。与早期主要依赖于局部证据的方法不同，我们通过许多可能属于最优树的不同点子集构建了一组候选树，然后根据结合图像证据和几何先验的全局目标函数选择最佳树。由于最优树不一定跨越所有点，因此该算法能够消除误报，同时保留正确的树拓扑结构。我们使用手动注释的明场显微镜照片、视网膜扫描和 DIADEM 挑战数据集来评估我们方法的性能。我们使用 DIADEM 度量来定量评估重建的拓扑准确性，并表明使用几何正则化可以显著提高性能。
