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An automated three-dimensional detection and segmentation method for touching cells by integrating concave points clustering and random walker algorithm.

He Y, Meng Y, Gong H, Chen S, Zhang B, Ding W, Luo Q, Li A - PLoS ONE (2014)

Bottom Line: 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.

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.

ABSTRACT
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.

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Related in: MedlinePlus

Seed point detection and segmentation on the K2 stack.The stack is a preprocessed binary image and is volume-rendered with the color-map's alpha values of 0.2. The black ring is a large vessel. (A) The results of the CPCC points (light green point). (B) The seed points (red points) of touching cells selected from the 26 cubic neighbor points of CPCC. (C) The seed points (light yellow points) of sparse cells obtained by extracting the local maximum of the Gaussian-convoluted image. (D) Results of the CC-random walker segmentation. Different cells are labeled in unique random colors.
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pone-0104437-g005: Seed point detection and segmentation on the K2 stack.The stack is a preprocessed binary image and is volume-rendered with the color-map's alpha values of 0.2. The black ring is a large vessel. (A) The results of the CPCC points (light green point). (B) The seed points (red points) of touching cells selected from the 26 cubic neighbor points of CPCC. (C) The seed points (light yellow points) of sparse cells obtained by extracting the local maximum of the Gaussian-convoluted image. (D) Results of the CC-random walker segmentation. Different cells are labeled in unique random colors.

Mentions: The results of the proposed algorithm on the K1 stack are shown in Figure 4, and the CPCC points are displayed in Figure 4A. Twelve seed points (the red points in Figure 4B) of touching cells were detected based on the CPCC points, and thirty seed points of sparse cells were detected by local maximum of Gaussian-convolved image (the light yellow points in Figure 4C). With unique random colors, the cell segmentation results are shown in Figure 4D, and all cells are labeled. The results of the K2 stack are shown in Figure 5. Cells were mostly touching on the K2 stack. Fifty-four seed points (the red points in Figure 5B) were detected using the CPCC points, and fifty-six seed points of sparse cells were identified (the light yellow points in Figure 5C). The segmentation results of K2 are shown in Figure 5D.


An automated three-dimensional detection and segmentation method for touching cells by integrating concave points clustering and random walker algorithm.

He Y, Meng Y, Gong H, Chen S, Zhang B, Ding W, Luo Q, Li A - PLoS ONE (2014)

Seed point detection and segmentation on the K2 stack.The stack is a preprocessed binary image and is volume-rendered with the color-map's alpha values of 0.2. The black ring is a large vessel. (A) The results of the CPCC points (light green point). (B) The seed points (red points) of touching cells selected from the 26 cubic neighbor points of CPCC. (C) The seed points (light yellow points) of sparse cells obtained by extracting the local maximum of the Gaussian-convoluted image. (D) Results of the CC-random walker segmentation. Different cells are labeled in unique random colors.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4128780&req=5

pone-0104437-g005: Seed point detection and segmentation on the K2 stack.The stack is a preprocessed binary image and is volume-rendered with the color-map's alpha values of 0.2. The black ring is a large vessel. (A) The results of the CPCC points (light green point). (B) The seed points (red points) of touching cells selected from the 26 cubic neighbor points of CPCC. (C) The seed points (light yellow points) of sparse cells obtained by extracting the local maximum of the Gaussian-convoluted image. (D) Results of the CC-random walker segmentation. Different cells are labeled in unique random colors.
Mentions: The results of the proposed algorithm on the K1 stack are shown in Figure 4, and the CPCC points are displayed in Figure 4A. Twelve seed points (the red points in Figure 4B) of touching cells were detected based on the CPCC points, and thirty seed points of sparse cells were detected by local maximum of Gaussian-convolved image (the light yellow points in Figure 4C). With unique random colors, the cell segmentation results are shown in Figure 4D, and all cells are labeled. The results of the K2 stack are shown in Figure 5. Cells were mostly touching on the K2 stack. Fifty-four seed points (the red points in Figure 5B) were detected using the CPCC points, and fifty-six seed points of sparse cells were identified (the light yellow points in Figure 5C). The segmentation results of K2 are shown in Figure 5D.

Bottom Line: 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.

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.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus