Automated Quantification Of Blood Microvessels In Hematoxylin And Eosin Whole Slide Images
Proceedings of the MICCAI Workshop on Computational Pathology, PMLR 156:94-104, 2021.
Tumour cells require resources to survive and proliferate. In order to be provided with a supportive micro-environment rich with resources to sustain optimal growth, tumour cells tend to reside in close proximity to a network of blood vessels. Quantification of blood microvessel density can be a useful measure to investigate the importance of resource limitation in tumours for prognostication and assigning treatment and mode of drug delivery. Currently, immunohistochemistry (IHC) with specific antibodies and the subsequent detection of its binding in the tumour tissue are used to identify microvessels. The automated quantification of blood microvessels in Hematoxylin and Eosin (H&E) stained images is not widely studied because microvessels are very complex and heterogeneous. In addition, their manual identification is tedious, time-consuming and subjective. We investigate whether the vasculature in H&E can be robustly identified in whole slide sections that would ultimately avoid the need for IHC and manual annotations. We propose an artificial intelligence model based on Generative Adversarial Networks (GAN) that, from an input H&E image, can generate a synthetic Erythroblast Transformation specific related gene (ERG) stained image, highlighting vessel structures. We also trained a spatially constrained Convolutional Neural Network (CNN) to identify single cells on ERG stained whole slide images, and found good concordance between detected cells in synthetic and real ERG. This pipeline was evaluated on 2002 image patches of size 2000x2000 pixels, sampled from 9 whole slide images. We achieved the mean $R^2$ of 0.70$\pm$0.14 in our testing set. This pipeline can pave the way to study proximity of tumour cells to blood vessels. This approach has the potential to reduce the use of IHC and tissues and enable large quantitative studies.