Artery segmentation and atherosclerotic plaque quantification using AI for murine whole slide images stained with oil red O
Abstract Atherosclerosis is the leading cause of death in Western industrial nations. To study the etiology of plaque progression, atherosclerotic mouse models are widely used. Traditionally, analyzing the obtained histological whole slide images of Oil Red O-stained aortic roots required manual seg...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-93967-6 |
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| author | Johann Christopher Engster Tobias Reinberger Nele Blum Pascal Stagge Thorsten M. Buzug Zouhair Aherrahrou Maik Stille |
| author_facet | Johann Christopher Engster Tobias Reinberger Nele Blum Pascal Stagge Thorsten M. Buzug Zouhair Aherrahrou Maik Stille |
| author_sort | Johann Christopher Engster |
| collection | DOAJ |
| description | Abstract Atherosclerosis is the leading cause of death in Western industrial nations. To study the etiology of plaque progression, atherosclerotic mouse models are widely used. Traditionally, analyzing the obtained histological whole slide images of Oil Red O-stained aortic roots required manual segmentation. To accelerate this process, an artificial intelligence-driven solution is proposed that comprises three stages: (1) defining the region of interest (ROI) of the aortic root using a YOLOv8l object detector, (2) applying supervised machine learning with ensembles of U-Net++ networks for artery segmentation using ROI masks, and (3) performing plaque segmentation within arterial walls with the unsupervised W-Net method. To establish a robust segmentation pipeline, we benchmark our methods using manually created masks ( $$\text {n}=6085$$ for artery segmentation, $$\text {n}=1089$$ for plaque segmentation). A key finding is that an ensemble of U-Net++ networks applied on ROI masks outperformed single network architectures. Through a novel combination strategy, the ensemble output can be easily modified, paving the way for a quick and robust application in the lab. Finally, a case study utilizing published mouse data ( $$\text {n}=373$$ slices) underscored the ability of our optimized pipeline to replicate human-made plaque predictions with a high correlation (Pearson’s $$\text {r}=0.91$$ ) and reproduce biological insights derived from manual analysis. |
| format | Article |
| id | doaj-art-6805eb0ff2ca4920b638468b956ca054 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-6805eb0ff2ca4920b638468b956ca0542025-08-20T03:13:54ZengNature PortfolioScientific Reports2045-23222025-04-0115111110.1038/s41598-025-93967-6Artery segmentation and atherosclerotic plaque quantification using AI for murine whole slide images stained with oil red OJohann Christopher Engster0Tobias Reinberger1Nele Blum2Pascal Stagge3Thorsten M. Buzug4Zouhair Aherrahrou5Maik Stille6Fraunhofer IMTE, Fraunhofer Research Institution for Individualized and Cell-Based Medical EngineeringInstitute for Cardiogenetics, University of LübeckFraunhofer IMTE, Fraunhofer Research Institution for Individualized and Cell-Based Medical EngineeringFraunhofer IMTE, Fraunhofer Research Institution for Individualized and Cell-Based Medical EngineeringFraunhofer IMTE, Fraunhofer Research Institution for Individualized and Cell-Based Medical EngineeringInstitute for Cardiogenetics, University of LübeckFraunhofer IMTE, Fraunhofer Research Institution for Individualized and Cell-Based Medical EngineeringAbstract Atherosclerosis is the leading cause of death in Western industrial nations. To study the etiology of plaque progression, atherosclerotic mouse models are widely used. Traditionally, analyzing the obtained histological whole slide images of Oil Red O-stained aortic roots required manual segmentation. To accelerate this process, an artificial intelligence-driven solution is proposed that comprises three stages: (1) defining the region of interest (ROI) of the aortic root using a YOLOv8l object detector, (2) applying supervised machine learning with ensembles of U-Net++ networks for artery segmentation using ROI masks, and (3) performing plaque segmentation within arterial walls with the unsupervised W-Net method. To establish a robust segmentation pipeline, we benchmark our methods using manually created masks ( $$\text {n}=6085$$ for artery segmentation, $$\text {n}=1089$$ for plaque segmentation). A key finding is that an ensemble of U-Net++ networks applied on ROI masks outperformed single network architectures. Through a novel combination strategy, the ensemble output can be easily modified, paving the way for a quick and robust application in the lab. Finally, a case study utilizing published mouse data ( $$\text {n}=373$$ slices) underscored the ability of our optimized pipeline to replicate human-made plaque predictions with a high correlation (Pearson’s $$\text {r}=0.91$$ ) and reproduce biological insights derived from manual analysis.https://doi.org/10.1038/s41598-025-93967-6Artery segmentationEnsembleMachine learningOil red OPlaque segmentation |
| spellingShingle | Johann Christopher Engster Tobias Reinberger Nele Blum Pascal Stagge Thorsten M. Buzug Zouhair Aherrahrou Maik Stille Artery segmentation and atherosclerotic plaque quantification using AI for murine whole slide images stained with oil red O Scientific Reports Artery segmentation Ensemble Machine learning Oil red O Plaque segmentation |
| title | Artery segmentation and atherosclerotic plaque quantification using AI for murine whole slide images stained with oil red O |
| title_full | Artery segmentation and atherosclerotic plaque quantification using AI for murine whole slide images stained with oil red O |
| title_fullStr | Artery segmentation and atherosclerotic plaque quantification using AI for murine whole slide images stained with oil red O |
| title_full_unstemmed | Artery segmentation and atherosclerotic plaque quantification using AI for murine whole slide images stained with oil red O |
| title_short | Artery segmentation and atherosclerotic plaque quantification using AI for murine whole slide images stained with oil red O |
| title_sort | artery segmentation and atherosclerotic plaque quantification using ai for murine whole slide images stained with oil red o |
| topic | Artery segmentation Ensemble Machine learning Oil red O Plaque segmentation |
| url | https://doi.org/10.1038/s41598-025-93967-6 |
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