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Aims: One of the major drivers of the adoption of digital pathology in clinical practice is the possibility of introducing digital image analysis (DIA) to assist with diagnostic tasks. This offers potential increases in accuracy, reproducibility, and efficiency. Whereas stand‐alone DIA has great potential benefit for research, little is known about the effect of DIA assistance in clinical use. The aim of this study was to investigate the clinical use characteristics of a DIA application for Ki67 proliferation assessment. Specifically, the human‐in‐the‐loop interplay between DIA and pathologists was studied. Methods and results: We retrospectively investigated breast cancer Ki67 areas assessed with human‐in‐the‐loop DIA and compared them with visual and automatic approaches. The results, expressed as standard deviation of the error in the Ki67 index, showed that visual estimation ('eyeballing') (14.9 percentage points) performed significantly worse (P < 0.05) than DIA alone (7.2 percentage points) and DIA with human‐in‐the‐loop corrections (6.9 percentage points). At the overall level, no improvement resulting from the addition of human‐in‐the‐loop corrections to the automatic DIA results could be seen. For individual cases, however, human‐in‐the‐loop corrections could address major DIA errors in terms of poor thresholding of faint staining and incorrect tumour–stroma separation. Conclusion: The findings indicate that the primary value of human‐in‐the‐loop corrections is to address major weaknesses of a DIA application, rather than fine‐tuning the DIA quantifications. [ABSTRACT FROM AUTHOR]

Background: Generating good training datasets is essential for machine learning-based nuclei detection methods. However, creating exhaustive nuclei contour annotations, to derive optimal training data from, is often infeasible. Methods: We compared different approaches for training nuclei detection methods solely based on nucleus center markers. Such markers contain less accurate information, especially with regard to nuclear boundaries, but can be produced much easier and in greater quantities. The approaches use different automated sample extraction methods to derive image positions and class labels from nucleus center markers. In addition, the approaches use different automated sample selection methods to improve the detection quality of the classification algorithm and reduce the run time of the training process. We evaluated the approaches based on a previously published generic nuclei detection algorithm and a set of Ki-67-stained breast cancer images. Results: A Voronoi tessellation-based sample extraction method produced the best performing training sets. However, subsampling of the extracted training samples was crucial. Even simple class balancing improved the detection quality considerably. The incorporation of active learning led to a further increase in detection quality. Conclusions: With appropriate sample extraction and selection methods, nuclei detection algorithms trained on the basis of simple center marker annotations can produce comparable quality to algorithms trained on conventionally created training sets. [ABSTRACT FROM AUTHOR]