Automated quality assessment of large digitised histology cohorts by artificial intelligence
Research using whole slide images (WSIs) of histopathology slides has increased exponentially over recent years. Glass slides from retrospective cohorts, some with patient follow-up data are digitised for the development and validation of artificial intelligence (AI) tools. Such resources, therefore...
| Main Authors: | , , , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Springer Nature
2022
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| _version_ | 1826307830111535104 |
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| author | Haghighat, M Browning, L Sirinukunwattana, K Malacrino, S Khalid Alham, N Colling, R Cui, Y Rakha, E Hamdy, FC Verrill, C Rittscher, J |
| author_facet | Haghighat, M Browning, L Sirinukunwattana, K Malacrino, S Khalid Alham, N Colling, R Cui, Y Rakha, E Hamdy, FC Verrill, C Rittscher, J |
| author_sort | Haghighat, M |
| collection | OXFORD |
| description | Research using whole slide images (WSIs) of histopathology slides has increased exponentially over recent years. Glass slides from retrospective cohorts, some with patient follow-up data are digitised for the development and validation of artificial intelligence (AI) tools. Such resources, therefore, become very important, with the need to ensure that their quality is of the standard necessary for downstream AI development. However, manual quality control of large cohorts of WSIs by visual assessment is unfeasible, and whilst quality control AI algorithms exist, these focus on bespoke aspects of image quality, e.g. focus, or use traditional machine-learning methods, which are unable to classify the range of potential image artefacts that should be considered. In this study, we have trained and validated a multi-task deep neural network to automate the process of quality control of a large retrospective cohort of prostate cases from which glass slides have been scanned several years after production, to determine both the usability of the images at the diagnostic level (considered in this study to be the minimal standard for research) and the common image artefacts present. Using a two-layer approach, quality overlays of WSIs were generated from a quality assessment (QA) undertaken at patch-level at 5× magnification. From these quality overlays the slide-level quality scores were predicted and then compared to those generated by three specialist urological pathologists, with a Pearson correlation of 0.89 for overall ‘usability’ (at a diagnostic level), and 0.87 and 0.82 for focus and H&E staining quality scores respectively. To demonstrate its wider potential utility, we subsequently applied our QA pipeline to the TCGA prostate cancer cohort and to a colorectal cancer cohort, for comparison. Our model, designated as PathProfiler, indicates comparable predicted usability of images from the cohorts assessed (86–90% of WSIs predicted to be usable), and perhaps more significantly is able to predict WSIs that could benefit from an intervention such as re-scanning or re-staining for quality improvement. We have shown in this study that AI can be used to automate the process of quality control of large retrospective WSI cohorts to maximise their utility for research. |
| first_indexed | 2024-03-07T07:08:57Z |
| format | Journal article |
| id | oxford-uuid:3c75c85d-b327-40c5-92d8-28418f6a588c |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:08:57Z |
| publishDate | 2022 |
| publisher | Springer Nature |
| record_format | dspace |
| spelling | oxford-uuid:3c75c85d-b327-40c5-92d8-28418f6a588c2022-06-07T11:49:37ZAutomated quality assessment of large digitised histology cohorts by artificial intelligenceJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:3c75c85d-b327-40c5-92d8-28418f6a588cEnglishSymplectic ElementsSpringer Nature2022Haghighat, MBrowning, LSirinukunwattana, KMalacrino, SKhalid Alham, NColling, RCui, YRakha, EHamdy, FCVerrill, CRittscher, JResearch using whole slide images (WSIs) of histopathology slides has increased exponentially over recent years. Glass slides from retrospective cohorts, some with patient follow-up data are digitised for the development and validation of artificial intelligence (AI) tools. Such resources, therefore, become very important, with the need to ensure that their quality is of the standard necessary for downstream AI development. However, manual quality control of large cohorts of WSIs by visual assessment is unfeasible, and whilst quality control AI algorithms exist, these focus on bespoke aspects of image quality, e.g. focus, or use traditional machine-learning methods, which are unable to classify the range of potential image artefacts that should be considered. In this study, we have trained and validated a multi-task deep neural network to automate the process of quality control of a large retrospective cohort of prostate cases from which glass slides have been scanned several years after production, to determine both the usability of the images at the diagnostic level (considered in this study to be the minimal standard for research) and the common image artefacts present. Using a two-layer approach, quality overlays of WSIs were generated from a quality assessment (QA) undertaken at patch-level at 5× magnification. From these quality overlays the slide-level quality scores were predicted and then compared to those generated by three specialist urological pathologists, with a Pearson correlation of 0.89 for overall ‘usability’ (at a diagnostic level), and 0.87 and 0.82 for focus and H&E staining quality scores respectively. To demonstrate its wider potential utility, we subsequently applied our QA pipeline to the TCGA prostate cancer cohort and to a colorectal cancer cohort, for comparison. Our model, designated as PathProfiler, indicates comparable predicted usability of images from the cohorts assessed (86–90% of WSIs predicted to be usable), and perhaps more significantly is able to predict WSIs that could benefit from an intervention such as re-scanning or re-staining for quality improvement. We have shown in this study that AI can be used to automate the process of quality control of large retrospective WSI cohorts to maximise their utility for research. |
| spellingShingle | Haghighat, M Browning, L Sirinukunwattana, K Malacrino, S Khalid Alham, N Colling, R Cui, Y Rakha, E Hamdy, FC Verrill, C Rittscher, J Automated quality assessment of large digitised histology cohorts by artificial intelligence |
| title | Automated quality assessment of large digitised histology cohorts by artificial intelligence |
| title_full | Automated quality assessment of large digitised histology cohorts by artificial intelligence |
| title_fullStr | Automated quality assessment of large digitised histology cohorts by artificial intelligence |
| title_full_unstemmed | Automated quality assessment of large digitised histology cohorts by artificial intelligence |
| title_short | Automated quality assessment of large digitised histology cohorts by artificial intelligence |
| title_sort | automated quality assessment of large digitised histology cohorts by artificial intelligence |
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