Abstract
Mitosis detection in hematoxylin and eosin (H&E) images is prone to error due to the unspecificity of the stain for this purpose. Alternatively, the inmunohistochemistry phospho-histone H3 (PHH3) stain has improved the task with a significant reduction of the false negatives. These facts point out on the interest in combining features from both stains to improve mitosis detection. Here we propose an algorithm that, taking as input a pair of whole-slides images (WSI) scanned from the same slide and stained with H&E and PHH3 respectively, find the matching between the stains of the same object. This allows to use both stains in the detection stage. Linear filtering in combination with local search based on a kd-tree structure is used to find potential matches between objects. A Siamese convolutional neural network (SCNN) is trained to detect the correct matches and a CNN model is trained for mitosis detection from matches. At the best of our knowledge, this is the first time that mitosis detection in WSI is assessed combining two stains. The experiments show a strong improvement of the detection F1-score when H&E and PHH3 are used jointly compared to the single stain F1-scores.
This paper has been supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (FEDER) under the grant DPI2016-77869-C2-2-R.
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López-Tapia, S., Olivencia, C., Aneiros-Fernández, J., Pérez de la Blanca, N. (2019). Improvement of Mitosis Detection Through the Combination of PHH3 and HE Features. In: Reyes-Aldasoro, C., Janowczyk, A., Veta, M., Bankhead, P., Sirinukunwattana, K. (eds) Digital Pathology. ECDP 2019. Lecture Notes in Computer Science(), vol 11435. Springer, Cham. https://doi.org/10.1007/978-3-030-23937-4_17
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