Bergler, M.M.BerglerBenz, M.M.BenzRauber, D.D.RauberHartmann, D.D.HartmannKötter, M.M.KötterEckstein, M.M.EcksteinSchneider-Stock, R.R.Schneider-StockHartmann, A.A.HartmannMerkel, S.S.MerkelBruns, V.V.BrunsWittenberg, T.T.WittenbergGeppert, C.C.Geppert2022-03-142022-03-142019https://publica.fraunhofer.de/handle/publica/41029310.1007/978-3-030-23937-4_102-s2.0-85069170776This contribution introduces a novel approach to the automatic detection of tumor buds in a digitalized pan-cytokeratin stained colorectal cancer slide. Tumor buds are representing an invasive pattern and are frequently investigated as a new diagnostic factor for measuring the aggressiveness of colorectal cancer. However, counting the number of buds under the microscope in a high power field by eyeballing is a strenuous, lengthy and error-prone task, whereas an automated solution could save time for the pathologists and enhance reproducibility. We propose a new hybrid method that consists of two steps. First possible tumor bud candidates are detected using a chain of classical image processing methods. Afterwards a convolutional deep neural network is applied to filter and reduce the number of false positive candidates detected in the first step. By comparing the automatically detected buds with a gold standard created by manual annotations, we gain a score of 0.977 for precision and 0.934 for sensitivity in our test sets on over 8.000 tumor buds.en621006conference paper