BACKGROUND: Understanding the cellular and molecular basis of tissue development and function requires analysis of individual cells while in their tissue context. METHODS: We developed software to find the optimum border around each cell (segmentation) from two-dimensional microscopic images of intact tissue. Samples were labeled with a fluorescent cell surface marker so that cell borders were brighter than elsewhere. The optimum border around each cell was defined as the border with an average intensity per unit length greater that any other possible border around that cell, and was calculated using the gray-weighted distance transform. Algorithm initiation requiring the user to mark two points per cell, one approximately in the center and the other on the border, ensured virtually 100% correct segmentation. Thereafter segmentation was automatic. RESULTS: The method was highly robust, because intermittent labeling of the cell borders, diffuse borders, and spurious signals away from the border do not significantly affect the optimum path. Computer-generated cells with increasing levels of added noise showed that the approach was accurate provided the cell could be detected visually. CONCLUSIONS: We have developed a highly robust algorithm for segmenting images of surface-labeled cells, enabling accurate and quantitative analysis of individual cells in tissue.
BACKGROUND: Understanding the cellular and molecular basis of tissue development and function requires analysis of individual cells while in their tissue context. METHODS: We developed software to find the optimum border around each cell (segmentation) from two-dimensional microscopic images of intact tissue. Samples were labeled with a fluorescent cell surface marker so that cell borders were brighter than elsewhere. The optimum border around each cell was defined as the border with an average intensity per unit length greater that any other possible border around that cell, and was calculated using the gray-weighted distance transform. Algorithm initiation requiring the user to mark two points per cell, one approximately in the center and the other on the border, ensured virtually 100% correct segmentation. Thereafter segmentation was automatic. RESULTS: The method was highly robust, because intermittent labeling of the cell borders, diffuse borders, and spurious signals away from the border do not significantly affect the optimum path. Computer-generated cells with increasing levels of added noise showed that the approach was accurate provided the cell could be detected visually. CONCLUSIONS: We have developed a highly robust algorithm for segmenting images of surface-labeled cells, enabling accurate and quantitative analysis of individual cells in tissue.
Authors: D P McCullough; P R Gudla; B S Harris; J A Collins; K J Meaburn; M A Nakaya; T P Yamaguchi; T Misteli; S J Lockett Journal: IEEE Trans Med Imaging Date: 2008-05 Impact factor: 10.048
Authors: Kaustav Nandy; Prabhakar R Gudla; Ryan Amundsen; Karen J Meaburn; Tom Misteli; Stephen J Lockett Journal: Cytometry A Date: 2012-07-31 Impact factor: 4.355