Semantic annotation for computational pathology: Multidisciplinary experience and best practice recommendations

Recent advances in whole slide imaging (WSI) technology have led to the development of a myriad of computer vision and artificial intelligence (AI) based diagnostic, prognostic, and predictive algorithms. Computational Pathology (CPath) offers an integrated solution to utilize information embedded i...

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Bibliographic Details
Published in:arXiv.org 2021-06
Main Authors: Wahab, Noorul, Miligy, Islam M, Dodd, Katherine, Sahota, Harvir, Toss, Michael, Lu, Wenqi, Jahanifar, Mostafa, Mohsin Bilal, Graham, Simon, Park, Young, Hadjigeorghiou, Giorgos, Bhalerao, Abhir, Lashen, Ayat, Ibrahim, Asmaa, Katayama, Ayaka, Ebili, Henry O, Parkin, Matthew, Sorell, Tom, Ahmed Raza, Shan E, Hero, Emily, Eldaly, Hesham, Tsang, Yee Wah, Gopalakrishnan, Kishore, Snead, David, Rakha, Emad, Rajpoot, Nasir, Minhas, Fayyaz
Format: Article
Language:English
Subjects:
Algorithms
Annotations
Artificial intelligence
Best practice
Computer vision
Consortia
Diagnostic systems
Guidelines
Machine learning
Pathology
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Summary:Recent advances in whole slide imaging (WSI) technology have led to the development of a myriad of computer vision and artificial intelligence (AI) based diagnostic, prognostic, and predictive algorithms. Computational Pathology (CPath) offers an integrated solution to utilize information embedded in pathology WSIs beyond what we obtain through visual assessment. For automated analysis of WSIs and validation of machine learning (ML) models, annotations at the slide, tissue and cellular levels are required. The annotation of important visual constructs in pathology images is an important component of CPath projects. Improper annotations can result in algorithms which are hard to interpret and can potentially produce inaccurate and inconsistent results. Despite the crucial role of annotations in CPath projects, there are no well-defined guidelines or best practices on how annotations should be carried out. In this paper, we address this shortcoming by presenting the experience and best practices acquired during the execution of a large-scale annotation exercise involving a multidisciplinary team of pathologists, ML experts and researchers as part of the Pathology image data Lake for Analytics, Knowledge and Education (PathLAKE) consortium. We present a real-world case study along with examples of different types of annotations, diagnostic algorithm, annotation data dictionary and annotation constructs. The analyses reported in this work highlight best practice recommendations that can be used as annotation guidelines over the lifecycle of a CPath project.
ISSN:2331-8422