An automated three-dimensional detection and segmentation method for touching cells by integrating concave points clustering and random walker algorithm.
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Characterizing cytoarchitecture is crucial for understanding brain functions and neural diseases.In neuroanatomy, it is an important task to accurately extract cell populations' centroids and contours.However, it is difficult to precisely segment numerous cells, especially those cells touching each other.
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Affiliation: Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China; MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China.
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Characterizing cytoarchitecture is crucial for understanding brain functions and neural diseases. In neuroanatomy, it is an important task to accurately extract cell populations' centroids and contours. Recent advances have permitted imaging at single cell resolution for an entire mouse brain using the Nissl staining method. However, it is difficult to precisely segment numerous cells, especially those cells touching each other. As presented herein, we have developed an automated three-dimensional detection and segmentation method applied to the Nissl staining data, with the following two key steps: 1) concave points clustering to determine the seed points of touching cells; and 2) random walker segmentation to obtain cell contours. Also, we have evaluated the performance of our proposed method with several mouse brain datasets, which were captured with the micro-optical sectioning tomography imaging system, and the datasets include closely touching cells. Comparing with traditional detection and segmentation methods, our approach shows promising detection accuracy and high robustness. Related in: MedlinePlus |
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Mentions: Next, we employed a strategy to obtain candidate seed points of touching cells. There is a method of determining them [34], but we used the CPCC point, Ĉm, to construct a cube whose side-length is 2×Rc (Rc corresponds to the cell radius, Rc = 7 µm) and the cube is centered on Ĉm. Then, total 26 key points of the cube were obtained, i.e., the eight vertexes, the centers of the six faces and the midpoints of the twelve sides. The 26 points are similar to the 26-connected neighbors of Ĉm. We called them the 26 cubic neighbor points of Ĉm and defined SD as the set of the 26 points: Si∈SD (0≤i≤25). Suppose the coordinates of Ĉm are (x, y, z), the coordinates of Si are (xi = x±Rc, yi = y±Rc, zi = z±Rc) and Si is the candidate seed point. In the 3D synthesized binary image stack, as shown in Figure 1C, the reddish purple points are the cubic neighbor points of two light green colored CPCC points, Ĉ1 and Ĉ2, and each CPCC point has 26 neighbor points. We show Ĉ1 and its 26 neighbor points alone in Figure 3A, and the three touching cells are named TC1, TC2 and TC3. The reddish purple points are the 26 cubic neighbor points (candidate seed points) of the first CPCC point, Ĉ1. In Figure 3C, the reddish purple points are the 26 cubic neighbor points (candidate seed points) of the second CPCC point, Ĉ2. |
View Article: PubMed Central - PubMed
Affiliation: Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China; MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China.