The number of nuclei on a pathology image assists pathologists in consistent diagnosis of breast cancer. Currently, most pathologists make a diagnosis based on a rough estimation of the number of nuclei on pathology images. Because of the rough estimation, the diagnosis is not objective. To assist pathologists to make a consistent, objective and fast diagnosis, it is necessary to develop a computer system to automatically recognize and count several kinds of nuclei. We have developed an algorithm for the automatic segmentation and counting of nuclei in breast cancer pathology images. In the development of the algorithm, we proposed two novel methods: an adaptive-sized hybrid neural network for the automatic segmentation of nuclei, insulin-like growth factor-II messenger RNAs and other structures, and the combined use of both the focused gradient filter and the watersheds algorithm for segmentation of overlapped nuclei.
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