  The development of deep segmentation models for computational pathology
(CPath) can help foster the investigation of interpretable morphological
biomarkers. Yet, there is a major bottleneck in the success of such approaches
because supervised deep learning models require an abundance of accurately
labelled data. This issue is exacerbated in the field of CPath because the
generation of detailed annotations usually demands the input of a pathologist
to be able to distinguish between different tissue constructs and nuclei.
Manually labelling nuclei may not be a feasible approach for collecting
large-scale annotated datasets, especially when a single image region can
contain thousands of different cells. However, solely relying on automatic
generation of annotations will limit the accuracy and reliability of ground
truth. Therefore, to help overcome the above challenges, we propose a
multi-stage annotation pipeline to enable the collection of large-scale
datasets for histology image analysis, with pathologist-in-the-loop refinement
steps. Using this pipeline, we generate the largest known nuclear instance
segmentation and classification dataset, containing nearly half a million
labelled nuclei in H&E stained colon tissue. We have released the dataset and
encourage the research community to utilise it to drive forward the development
of downstream cell-based models in CPath.
