Abstract
Morphological assessment of glands in histopathology images is very important in cancer grading. However, this is labour intensive, requires highly trained pathologists and has limited reproducibility. Digitisation of tissue slides provides us with the opportunity to employ computers, which are very efficient in repetitive tasks, allowing us to automate the morphological assessment with input from the pathologist. The first step in automated morphological assessment is the segmentation of these glandular regions. In this paper, we present a multi-input multi-output convolutional neural network for segmentation of glands in histopathology images. We test our algorithm on the publicly available GLaS data set and show that our algorithm produces competitive results compared to the state-of-the-art algorithms in terms of various quantitative measures.
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Acknowledgements
We are grateful to the BBSRC UK for supporting this study through project grant BB/K018868/1.
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Raza, S.E.A., Cheung, L., Epstein, D., Pelengaris, S., Khan, M., Rajpoot, N.M. (2017). MIMONet: Gland Segmentation Using Multi-Input-Multi-Output Convolutional Neural Network. In: Valdés Hernández, M., González-Castro, V. (eds) Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-60964-5_61
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DOI: https://doi.org/10.1007/978-3-319-60964-5_61
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