AI2Seg: A Method and Tool for AI-based Annotation Inspection of Biomedical Instance Segmentation Datasets
Schilling, Marcel P.; Klinger, Lukas; Schumacher, Ulrike; Schmelzer, Svenja; López, Miguel Bordallo; Nestler, Britta; Reischl, Markus (2022-12-11)
IEEE
11.12.2022
M. P. Schilling et al., "AI2Seg: A Method and Tool for AI-based Annotation Inspection of Biomedical Instance Segmentation Datasets," 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Sydney, Australia, 2023, pp. 1-6, doi: 10.1109/EMBC40787.2023.10341074
https://creativecommons.org/licenses/by/3.0/
© 2023 IEEE. This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/.
https://creativecommons.org/licenses/by/3.0/
© 2023 IEEE. This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/.
https://creativecommons.org/licenses/by/3.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202404182835
https://urn.fi/URN:NBN:fi:oulu-202404182835
Tiivistelmä
Abstract
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 2 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.
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 2 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.
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