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

Oliver Schoppe (), Chenchen Pan, Javier Coronel, Hongcheng Mai, Zhouyi Rong, Mihail Ivilinov Todorov, Annemarie Müskes, Fernando Navarro, Hongwei Li, Ali Ertürk () and Bjoern H. Menze ()
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Oliver Schoppe: Technical University of Munich
Chenchen Pan: Helmholtz Zentrum München
Javier Coronel: Technical University of Munich
Hongcheng Mai: Helmholtz Zentrum München
Zhouyi Rong: Helmholtz Zentrum München
Mihail Ivilinov Todorov: Helmholtz Zentrum München
Annemarie Müskes: Charité, Universitätsmedizin Berlin
Fernando Navarro: Technical University of Munich
Hongwei Li: Technical University of Munich
Ali Ertürk: Helmholtz Zentrum München
Bjoern H. Menze: Technical University of Munich

Nature Communications, 2020, vol. 11, issue 1, 1-14

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-19449-7

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