Applying machine learning to assist in the morphometric assessment of brain arteriolosclerosis through automation
Objective quantification of brain arteriolosclerosis remains an area of ongoing refinement in neuropathology, with current methods primarily utilizing semi-quantitative scales completed through manual histological examination. These approaches offer modest inter-rater reliability and do not provide...
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University of Münster / Open Journals System
2025-06-01
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| Series: | Free Neuropathology |
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| Online Access: | https://www.uni-muenster.de/Ejournals/index.php/fnp/article/view/6387 |
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| author | Jerry J. Lou Peter Chang Kiana D. Nava Chanon Chantaduly Hsin-Pei Wang William H. Yong Viharkumar Patel Ajinkya J. Chaudhari La Rissa Vasquez Edwin Monuki Elizabeth Head Harry V. Vinters Shino Magaki Danielle J. Harvey Chen-Nee Chuah Charles S. DeCarli Christopher K. Williams Michael Keiser Brittany N. Dugger |
| author_facet | Jerry J. Lou Peter Chang Kiana D. Nava Chanon Chantaduly Hsin-Pei Wang William H. Yong Viharkumar Patel Ajinkya J. Chaudhari La Rissa Vasquez Edwin Monuki Elizabeth Head Harry V. Vinters Shino Magaki Danielle J. Harvey Chen-Nee Chuah Charles S. DeCarli Christopher K. Williams Michael Keiser Brittany N. Dugger |
| author_sort | Jerry J. Lou |
| collection | DOAJ |
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Objective quantification of brain arteriolosclerosis remains an area of ongoing refinement in neuropathology, with current methods primarily utilizing semi-quantitative scales completed through manual histological examination. These approaches offer modest inter-rater reliability and do not provide precise quantitative metrics. To address this gap, we present a prototype end-to-end machine learning (ML)-based algorithm, Arteriolosclerosis Segmentation (ArtSeg), followed by Vascular Morphometry (VasMorph) – to assist persons in the morphometric analysis of arteriolosclerotic vessels on whole slide images (WSIs). We digitized hematoxylin and eosin-stained glass slides (13 participants, total 42 WSIs) of human brain frontal or occipital lobe cortical and/or periventricular white matter collected from three brain banks (University of California, Davis, Irvine, and Los Angeles Alzheimer’s Disease Research Centers). ArtSeg comprises three ML models for blood vessel detection, arteriolosclerosis classification, and segmentation of arteriolosclerotic vessel walls and lumens. For blood vessel detection, ArtSeg achieved area under the receiver operating characteristic curve (AUC-ROC) values of 0.79 (internal hold-out testing) and 0.77 (external testing), Dice scores of 0.56 (internal hold-out) and 0.74 (external), and Hausdorff distances of 2.53 (internal hold-out) and 2.15 (external). Arteriolosclerosis classification demonstrated accuracies of 0.94 (mean, 3-fold cross-validation), 0.86 (internal hold-out), and 0.77 (external), alongside AUC-ROC values of 0.69 (mean, 3-fold cross-validation), 0.87 (internal hold-out), and 0.83 (external). For arteriolosclerotic vessel segmentation, ArtSeg yielded Dice scores of 0.68 (mean, 3-fold cross-validation), 0.73 (internal hold-out), and 0.71 (external); Hausdorff distances of 7.63 (mean, 3-fold cross-validation), 6.93 (internal hold-out), and 7.80 (external); and AUC-ROC values of 0.90 (mean, 3-fold cross-validation), 0.92 (internal hold-out), and 0.87 (external). VasMorph successfully derived sclerotic indices, vessel wall thicknesses, and vessel wall to lumen area ratios from ArtSeg-segmented vessels, producing results comparable to expert assessment. This integrated approach shows promise as an assistive tool to enhance current neuropathological evaluation of brain arteriolosclerosis, offering potential for improved inter-rater reliability and quantification.
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| format | Article |
| id | doaj-art-0e31e203068d445aaaa7bb1834e13b3a |
| institution | OA Journals |
| issn | 2699-4445 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | University of Münster / Open Journals System |
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| series | Free Neuropathology |
| spelling | doaj-art-0e31e203068d445aaaa7bb1834e13b3a2025-08-20T02:30:34ZengUniversity of Münster / Open Journals SystemFree Neuropathology2699-44452025-06-01610.17879/freeneuropathology-2025-6387Applying machine learning to assist in the morphometric assessment of brain arteriolosclerosis through automationJerry J. Lou0Peter Chang1Kiana D. Nava2Chanon Chantaduly3Hsin-Pei Wang4William H. Yong5Viharkumar Patel6Ajinkya J. Chaudhari7La Rissa Vasquez8Edwin Monuki9Elizabeth Head10Harry V. Vinters11Shino Magaki12Danielle J. Harvey13Chen-Nee Chuah14Charles S. DeCarli15Christopher K. Williams16Michael Keiser17Brittany N. Dugger18Department of Pathology and Laboratory Medicine, School of Medicine, University of California Irvine, Irvine, USADepartment of Radiological Sciences, Center for Artificial Intelligence in Diagnostic Medicine, School of Medicine, University of California Irvine, Orange, USADepartment of Pathology and Laboratory Medicine, School of Medicine, University of California Davis, Sacramento, USADepartment of Radiological Sciences, Center for Artificial Intelligence in Diagnostic Medicine, School of Medicine, University of California Irvine, Orange, USADepartment of Pathology and Laboratory Medicine, School of Medicine, University of California Davis, Sacramento, USADepartment of Pathology and Laboratory Medicine, School of Medicine, University of California Irvine, Irvine, USADepartment of Pathology and Laboratory Medicine, School of Medicine, University of California Davis, Sacramento, USADepartment of Electrical and Computer Engineering, University of California, Davis, USADepartment of Pathology and Laboratory Medicine, School of Medicine, University of California Davis, Sacramento, USADepartment of Pathology and Laboratory Medicine, School of Medicine, University of California Irvine, Irvine, USADepartment of Pathology and Laboratory Medicine, School of Medicine, University of California Irvine, Irvine, USADepartment of Pathology and Laboratory Medicine and Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USADepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USADepartment of Public Health Sciences, School of Medicine, University of California Davis, Davis, USADepartment of Electrical and Computer Engineering, University of California Davis, Davis, USADepartment of Neurology, School of Medicine, University of California Davis, Sacramento, USADepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USADepartment of Pharmaceutical Chemistry, Department of Bioengineering and Therapeutic Sciences, Institute for Neurodegenerative Diseases; Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, USADepartment of Pathology and Laboratory Medicine, School of Medicine, University of California Davis, Sacramento, USA Objective quantification of brain arteriolosclerosis remains an area of ongoing refinement in neuropathology, with current methods primarily utilizing semi-quantitative scales completed through manual histological examination. These approaches offer modest inter-rater reliability and do not provide precise quantitative metrics. To address this gap, we present a prototype end-to-end machine learning (ML)-based algorithm, Arteriolosclerosis Segmentation (ArtSeg), followed by Vascular Morphometry (VasMorph) – to assist persons in the morphometric analysis of arteriolosclerotic vessels on whole slide images (WSIs). We digitized hematoxylin and eosin-stained glass slides (13 participants, total 42 WSIs) of human brain frontal or occipital lobe cortical and/or periventricular white matter collected from three brain banks (University of California, Davis, Irvine, and Los Angeles Alzheimer’s Disease Research Centers). ArtSeg comprises three ML models for blood vessel detection, arteriolosclerosis classification, and segmentation of arteriolosclerotic vessel walls and lumens. For blood vessel detection, ArtSeg achieved area under the receiver operating characteristic curve (AUC-ROC) values of 0.79 (internal hold-out testing) and 0.77 (external testing), Dice scores of 0.56 (internal hold-out) and 0.74 (external), and Hausdorff distances of 2.53 (internal hold-out) and 2.15 (external). Arteriolosclerosis classification demonstrated accuracies of 0.94 (mean, 3-fold cross-validation), 0.86 (internal hold-out), and 0.77 (external), alongside AUC-ROC values of 0.69 (mean, 3-fold cross-validation), 0.87 (internal hold-out), and 0.83 (external). For arteriolosclerotic vessel segmentation, ArtSeg yielded Dice scores of 0.68 (mean, 3-fold cross-validation), 0.73 (internal hold-out), and 0.71 (external); Hausdorff distances of 7.63 (mean, 3-fold cross-validation), 6.93 (internal hold-out), and 7.80 (external); and AUC-ROC values of 0.90 (mean, 3-fold cross-validation), 0.92 (internal hold-out), and 0.87 (external). VasMorph successfully derived sclerotic indices, vessel wall thicknesses, and vessel wall to lumen area ratios from ArtSeg-segmented vessels, producing results comparable to expert assessment. This integrated approach shows promise as an assistive tool to enhance current neuropathological evaluation of brain arteriolosclerosis, offering potential for improved inter-rater reliability and quantification. https://www.uni-muenster.de/Ejournals/index.php/fnp/article/view/6387Machine learningArtificial intelligenceNeuropathologyArteriolosclerosisBlood vesselMorphometry |
| spellingShingle | Jerry J. Lou Peter Chang Kiana D. Nava Chanon Chantaduly Hsin-Pei Wang William H. Yong Viharkumar Patel Ajinkya J. Chaudhari La Rissa Vasquez Edwin Monuki Elizabeth Head Harry V. Vinters Shino Magaki Danielle J. Harvey Chen-Nee Chuah Charles S. DeCarli Christopher K. Williams Michael Keiser Brittany N. Dugger Applying machine learning to assist in the morphometric assessment of brain arteriolosclerosis through automation Free Neuropathology Machine learning Artificial intelligence Neuropathology Arteriolosclerosis Blood vessel Morphometry |
| title | Applying machine learning to assist in the morphometric assessment of brain arteriolosclerosis through automation |
| title_full | Applying machine learning to assist in the morphometric assessment of brain arteriolosclerosis through automation |
| title_fullStr | Applying machine learning to assist in the morphometric assessment of brain arteriolosclerosis through automation |
| title_full_unstemmed | Applying machine learning to assist in the morphometric assessment of brain arteriolosclerosis through automation |
| title_short | Applying machine learning to assist in the morphometric assessment of brain arteriolosclerosis through automation |
| title_sort | applying machine learning to assist in the morphometric assessment of brain arteriolosclerosis through automation |
| topic | Machine learning Artificial intelligence Neuropathology Arteriolosclerosis Blood vessel Morphometry |
| url | https://www.uni-muenster.de/Ejournals/index.php/fnp/article/view/6387 |
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