Segmentation and analysis of breast cancer pathological images by an adaptive-sized hybrid neural network

Akira Hasegawa; Kevin J. Cullen M.D.; Seong Ki Mun

doi:10.1117/12.237980

16 April 1996 Segmentation and analysis of breast cancer pathological images by an adaptive-sized hybrid neural network

Akira Hasegawa, Kevin J. Cullen M.D., Seong Ki Mun

Proceedings Volume 2710, Medical Imaging 1996: Image Processing; (1996) https://doi.org/10.1117/12.237980
Event: Medical Imaging 1996, 1996, Newport Beach, CA, United States

Abstract

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.

Citation Download Citation

Akira Hasegawa, Kevin J. Cullen M.D., and Seong Ki Mun "Segmentation and analysis of breast cancer pathological images by an adaptive-sized hybrid neural network", Proc. SPIE 2710, Medical Imaging 1996: Image Processing, (16 April 1996); https://doi.org/10.1117/12.237980

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