A Deep Learning Pipeline to Delineate Proliferative Areas of Intracranial Tumors in Digital Slides

Separating tumor cells from other tissues such as meninges and blood is one of the vital steps towards automated quantification of the proliferative index in digital slides of brain tumors. In this paper, we present a deep learning based pipeline to delineate areas of tumor in meningioma and oligodendroglioma specimens stained with Ki-67 marker. A pre-trained convolutional neural network (CNN) was fine-tuned with 7057 image tiles to classify whole slide images (n = 15) in a tile-by-tile mode. The performance of the model was evaluated on slides manually annotated by the pathologist. The CNN model detected tumor areas with 89.4% accuracy. Areas with blood and meninges were respectively classified with 98.2% and 89.8% accuracy. The overall classification accuracy was 88.7%, and the Cohen’s kappa coefficient reached 0.748, indicating a very good concordance with the manual ground truth. Our pipeline can process digital slides at full resolution, and has the potential to objectively pre-process slides for proliferative index quantification.