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Deep learning-enabled multi-organ segmentation in whole-body mouse scans


Schoppe, Oliver; Pan, Chenchen; Coronel, Javier; Mai, Hongcheng; Rong, Zhouyi; Todorov, Mihail Ivilinov; Müskes, Annemarie; Navarro, Fernando; Li, Hongwei; Ertürk, Ali; Menze, Bjoern H (2020). Deep learning-enabled multi-organ segmentation in whole-body mouse scans. Nature Communications, 11:5626.

Abstract

Whole-body imaging of mice is a key source of information for research. Organ segmentation is a prerequisite for quantitative analysis but is a tedious and error-prone task if done manually. Here, we present a deep learning solution called AIMOS that automatically segments major organs (brain, lungs, heart, liver, kidneys, spleen, bladder, stomach, intestine) and the skeleton in less than a second, orders of magnitude faster than prior algorithms. AIMOS matches or exceeds the segmentation quality of state-of-the-art approaches and of human experts. We exemplify direct applicability for biomedical research for localizing cancer metastases. Furthermore, we show that expert annotations are subject to human error and bias. As a consequence, we show that at least two independently created annotations are needed to assess model performance. Importantly, AIMOS addresses the issue of human bias by identifying the regions where humans are most likely to disagree, and thereby localizes and quantifies this uncertainty for improved downstream analysis. In summary, AIMOS is a powerful open-source tool to increase scalability, reduce bias, and foster reproducibility in many areas of biomedical research.

Abstract

Whole-body imaging of mice is a key source of information for research. Organ segmentation is a prerequisite for quantitative analysis but is a tedious and error-prone task if done manually. Here, we present a deep learning solution called AIMOS that automatically segments major organs (brain, lungs, heart, liver, kidneys, spleen, bladder, stomach, intestine) and the skeleton in less than a second, orders of magnitude faster than prior algorithms. AIMOS matches or exceeds the segmentation quality of state-of-the-art approaches and of human experts. We exemplify direct applicability for biomedical research for localizing cancer metastases. Furthermore, we show that expert annotations are subject to human error and bias. As a consequence, we show that at least two independently created annotations are needed to assess model performance. Importantly, AIMOS addresses the issue of human bias by identifying the regions where humans are most likely to disagree, and thereby localizes and quantifies this uncertainty for improved downstream analysis. In summary, AIMOS is a powerful open-source tool to increase scalability, reduce bias, and foster reproducibility in many areas of biomedical research.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Department of Quantitative Biomedicine
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Physical Sciences > General Chemistry
Life Sciences > General Biochemistry, Genetics and Molecular Biology
Physical Sciences > General Physics and Astronomy
Uncontrolled Keywords:General Biochemistry, Genetics and Molecular Biology, General Physics and Astronomy, General Chemistry
Language:English
Date:1 December 2020
Deposited On:29 Jan 2021 15:57
Last Modified:01 Feb 2021 16:31
Publisher:Nature Publishing Group
ISSN:2041-1723
OA Status:Gold
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1038/s41467-020-19449-7
PubMed ID:33159057

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