Determination of the Mitotically Most Active Region for Computer-Aided Mitotic Count

Manual count of mitotic figures, which is determined in the tumor region with the highest mitotic activity, is a key parameter of most grading schemes. It can be, however, strongly dependent on the area selection due to uneven mitotic figure distribution. We aimed to assess the question, how significantly the area selection impacts the mitotic count, which has a known high inter-rater disagreement. On a data set of 32 whole slide images of H&E-stained canine cutaneous mast cell tumor, fully annotated for mitotic figures, we asked 8 veterinary pathologists (5 board-certified, 3 in training) to select a field of interest for the mitotic count. To assess the potential difference in grading, we compared the mitotic count of the selected regions to the overall distribution on the slide. Additionally, we evaluated three deep learning-based methods on the same task: In one approach, the model would directly try to predict the mitotic count for the presented image patches as a regression task. The second method aims at deriving a segmentation mask for mitotic figures, which is then used to obtain a mitotic density. Finally, we evaluated a two-stage object-detection pipeline based on state-of-the-art architectures to identify individual mitotic figures. We found that the predictions by all models were, on average, better than those of the experts. The two-stage object detector performed best and outperformed most of the human experts on the majority of tumor cases. The correlation between the predicted and the ground truth mitotic count was also best for this approach (0.963 to 0.979). Further, we found considerable differences in position selection between experts, which could partially explain the high variance that has been reported for the manual mitotic count. To achieve better inter-rater agreement, we propose to use a computer-based area selection for the manual mitotic count.