AI 2 Seg: A Method and Tool for AI-based Annotation Inspection of Biomedical Instance Segmentation Datasets

In biomedical engineering, deep neural networks are commonly used for the diagnosis and assessment of diseases through the interpretation of medical images. The effectiveness of these networks relies heavily on the availability of annotated datasets for training. However, obtaining noise-free and co...

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Bibliographic Details
Published inAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Vol. 2023; p. 1
Main Authors Schilling, Marcel P, Klinger, Lukas, Schumacher, Ulrike, Schmelzer, Svenja, Lopez, Miguel Bordallo, Nestler, Britta, Reischl, Markus
Format Journal Article
LanguageEnglish
Published United States 01.07.2023
Subjects
Algorithms
Bioengineering
Biomedical Engineering
Health Personnel
Humans
Neural Networks, Computer
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Summary:In biomedical engineering, deep neural networks are commonly used for the diagnosis and assessment of diseases through the interpretation of medical images. The effectiveness of these networks relies heavily on the availability of annotated datasets for training. However, obtaining noise-free and consistent annotations from experts, such as pathologists, radiologists, and biologists, remains a significant challenge. One common task in clinical practice and biological imaging applications is instance segmentation. Though, there is currently a lack of methods and open-source tools for the automated inspection of biomedical instance segmentation datasets concerning noisy annotations. To address this issue, we propose a novel deep learning-based approach for inspecting noisy annotations and provide an accompanying software implementation, AI Seg, to facilitate its use by domain experts. The performance of the proposed algorithm is demonstrated on the medical MoNuSeg dataset and the biological LIVECell dataset.
ISSN:2694-0604