IEEE J Biomed Health Inform. 2022 Jul;26(7):3092-3103. doi: 10.1109/JBHI.2022.3147512. Epub 2022 Jul 1.
Neuron tracing from optical image is critical in understanding brain function in diseases. A key problem is to trace discontinuous filamentary structures from noisy background, which is commonly encountered in neuronal and some medical images. Broken traces lead to cumulative topological errors, and current methods were hard to assemble various fragmentary traces for correct connection. In this paper, we propose a graph connectivity theoretical method for precise filamentary structure tracing in neuron image. First, we build the initial subgraphs of signals via a region-to-region based tracing method on CNN predicted probability. CNN technique removes noise interference, whereas its prediction for some elongated fragments is still incomplete. Second, we reformulate the global connection problem of individual or fragmented subgraphs under heuristic graph restrictions as a dynamic linear programming function via minimizing graph connectivity cost, where the connected cost of breakpoints are calculated using their probability strength via minimum cost path. Experimental results on challenging neuronal images proved that the proposed method outperformed existing methods and achieved similar results of manual tracing, even in some complex discontinuous issues. Performances on vessel images indicate the potential of the method for some other tubular objects tracing.
神经元光学图像示踪对于理解疾病中的大脑功能至关重要。一个关键问题是从噪声背景中追踪不连续的丝状结构，这在神经元和一些医学图像中很常见。断裂的轨迹会导致累积的拓扑错误，而当前的方法很难将各种零碎的轨迹组装起来以实现正确的连接。在本文中，我们提出了一种基于图连通性理论的方法，用于精确追踪神经元图像中的丝状结构。首先，我们通过基于区域到区域的跟踪方法在 CNN 预测概率上构建信号的初始子图。CNN 技术可以去除噪声干扰，但对一些细长片段的预测仍然不完整。其次，我们通过最小化图连通性成本，将个体或碎片化子图的全局连接问题重新表述为一个启发式图约束下的动态线性规划函数，其中通过最小成本路径计算断点的连接成本，该路径的成本使用其概率强度进行计算。在具有挑战性的神经元图像上的实验结果表明，该方法优于现有方法，并且在某些复杂的不连续问题上甚至可以达到手动追踪的效果。在血管图像上的性能表明，该方法对于一些其他管状物体的追踪具有潜力。
