Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification

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 l...

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Bibliographic Details
Published in:arXiv.org 2021-11
Main Authors: Graham, Simon, Jahanifar, Mostafa, Azam, Ayesha, Nimir, Mohammed, Yee-Wah Tsang, Dodd, Katherine, Hero, Emily, Sahota, Harvir, Tank, Atisha, Benes, Ksenija, Wahab, Noorul, Minhas, Fayyaz, Ahmed Raza, Shan E, Hesham El Daly, Gopalakrishnan, Kishore, Snead, David, Rajpoot, Nasir
Format: Article
Language:English
Subjects:
Biomarkers
Classification
Colon
Datasets
Histology
Image analysis
Image annotation
Image segmentation
Instance segmentation
Machine learning
Nuclei (cytology)
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Summary: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.
ISSN:2331-8422