Zusammenfassung
Histopathological prognostication of neoplasia including most tumor grading systems are based upon a number of criteria. Probably the most important is the number of mitotic figures which are most commonly determined as the mitotic count (MC), i.e. number of mitotic figures within 10 consecutive high power fields. Often the area with the highest mitotic activity is to be selected for the MC. However, since mitotic activity is not known in advance, an arbitrary choice of this region is considered one important cause for high variability in the prognostication and grading. In this work, we present an algorithmic approach that first calculates a mitotic cell map based upon a deep convolutional network. This map is in a second step used to construct a mitotic activity estimate. Lastly, we select the image segment representing the size of ten high power fields with the overall highest mitotic activity as a region proposal for an expert MC determination. We evaluate the approach using a dataset of 32 completely annotated whole slide images, where 22 were used for training of the network and 10 for test. We find a correlation of r=0.936 in mitotic count estimate.
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Literatur
Meuten DJ, Moore FM, George JW. Mitotic count and the field of view area. Vet Pathol. 2016;53(1):7-9.
Boiesen P, Bendahl PO, Anagnostaki L, et al. Histologic grading in breast cancer: reproducibility between seven pathologic departments. Acta Oncol. 2000;39(1):41-45.
Bertram CA, Gurtner C, Dettwiler M, et al. Validation of digital microscopy compared with light microscopy for the diagnosis of canine cutaneous tumors. Vet Pathol. 2018;55(4):490-500.
Bonert M, Tate AJ. Mitotic counts in breast cancer should be standardized with a uniform sample area. Biomed Eng Online. 2017;16(1):28.
Cireşan DC, Giusti A, Gambardella LM, et al. Mitosis detection in breast cancer histology images with deep neural networks. Proc MICCAI. 2013;16:411-418.
Paeng K, Hwang S, Park S, et al. A unified framework for tumor proliferation score prediction in breast histopathology. Deep Learn Med Image Anal Multimod Learn Clin Decis Support. 2017; p. 231-239.
Li C, Wang X, Liu W, et al. DeepMitosis: mitosis detection via deep detection, verification and segmentation networks. Med Image Anal. 2018;45:121-133.
Aubreville M, Bertram CA, Klopeisch R, et al. SlideRunner - a tool for massive cell annotations in whole slide images. Proc BVM. 2018; p. 309-314.
Ronneberger O, Fischer P, Brox T. U-net - convolutional networks for biomedical image segmentation. Proc MICCAI. 2015;9351(Chapter 28):234-241.
Rahman MA,Wang Y. Optimizing intersection-over-union in deep neural networks for image segmentation. Int Symp Vis Comput. 2016; p. 234-244.
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© 2019 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Aubreville, M., Bertram, C.A., Klopfleisch, R., Maier, A. (2019). Augmented Mitotic Cell Count Using Field of Interest Proposal. In: Handels, H., Deserno, T., Maier, A., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2019. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-25326-4_71
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DOI: https://doi.org/10.1007/978-3-658-25326-4_71
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