Abstract
Detection of epithelial tumor nuclei in standard Hematoxylin & Eosin stained histology images is an essential step for the analysis of tissue architecture. The problem is quite challenging due to the high chromatin texture of the tumor nuclei and their irregular size and shape. In this work, we propose a spatially constrained convolutional neural network (CNN) for the detection of malignant epithelial nuclei in histology images. Given an input patch, the proposed CNN is trained to regress, for every pixel in the patch, the probability of being the center of an epithelial tumor nucleus. The estimated probability values are topologically constrained such that high probability values are concentrated in the vicinity of the center of nuclei. The location of local maxima is then used as a cue for the final detection. Experimental results show that the proposed network outperforms the conventional CNN with center-pixel-only regression for the task of epithelial tumor nuclei detection.
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This paper was made possible by NPRP grant number NPRP5-1345-1-228 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
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Sirinukunwattana, K., Ahmed Raza, S.E., Tsang, YW., Snead, D., Cree, I., Rajpoot, N. (2015). A Spatially Constrained Deep Learning Framework for Detection of Epithelial Tumor Nuclei in Cancer Histology Images. In: Wu, G., Coupé, P., Zhan, Y., Munsell, B., Rueckert, D. (eds) Patch-Based Techniques in Medical Imaging. Patch-MI 2015. Lecture Notes in Computer Science(), vol 9467. Springer, Cham. https://doi.org/10.1007/978-3-319-28194-0_19
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DOI: https://doi.org/10.1007/978-3-319-28194-0_19
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