Artificial intelligence assisted compositional analyses of human abdominal aortic aneurysms ex vivo
Quantification of histological information from excised human abdominal aortic aneurysm (AAA) specimens may provide essential information on the degree of infiltration of inflammatory cells in different regions of the AAA. Such information will support mechanistic insight in AAA pathology and can be...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-08-01
|
| Series: | Frontiers in Physiology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2022.840965/full |
| _version_ | 1828322220260196352 |
|---|---|
| author | Bjarne Thorsted Lisette Bjerregaard Pia S. Jensen Pia S. Jensen Pia S. Jensen Lars M. Rasmussen Lars M. Rasmussen Lars M. Rasmussen Jes S. Lindholt Jes S. Lindholt Maria Bloksgaard |
| author_facet | Bjarne Thorsted Lisette Bjerregaard Pia S. Jensen Pia S. Jensen Pia S. Jensen Lars M. Rasmussen Lars M. Rasmussen Lars M. Rasmussen Jes S. Lindholt Jes S. Lindholt Maria Bloksgaard |
| author_sort | Bjarne Thorsted |
| collection | DOAJ |
| description | Quantification of histological information from excised human abdominal aortic aneurysm (AAA) specimens may provide essential information on the degree of infiltration of inflammatory cells in different regions of the AAA. Such information will support mechanistic insight in AAA pathology and can be linked to clinical measures for further development of AAA treatment regimens. We hypothesize that artificial intelligence can support high throughput analyses of histological sections of excised human AAA. We present an analysis framework based on supervised machine learning. We used TensorFlow and QuPath to determine the overall architecture of the AAA: thrombus, arterial wall, and adventitial loose connective tissue. Within the wall and adventitial zones, the content of collagen, elastin, and specific inflammatory cells was quantified. A deep neural network (DNN) was trained on manually annotated, Weigert stained, tissue sections (14 patients) and validated on images from two other patients. Finally, we applied the method on 95 new patient samples. The DNN was able to segment the sections according to the overall wall architecture with Jaccard coefficients after 65 epocs of 92% for the training and 88% for the validation data set, respectively. Precision and recall both reached 92%. The zone areas were highly variable between patients, as were the outputs on total cell count and elastin/collagen fiber content. The number of specific cells or stained area per zone was deterministically determined. However, combining the masks based on the Weigert stainings, with images of immunostained serial sections requires addition of landmark recognition to the analysis path. The combination of digital pathology, the DNN we developed, and landmark registration will provide a strong tool for future analyses of the histology of excised human AAA. In combination with biomechanical testing and microstructurally motivated mathematical models of AAA remodeling, the method has the potential to be a strong tool to provide mechanistic insight in the disease. In combination with each patients’ demographic and clinical profile, the method can be an interesting tool to in supportof a better treatment regime for the patients. |
| first_indexed | 2024-04-13T18:37:55Z |
| format | Article |
| id | doaj.art-50868be115dc47a9b3a7c95ab098c010 |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-04-13T18:37:55Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-50868be115dc47a9b3a7c95ab098c0102022-12-22T02:34:49ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2022-08-011310.3389/fphys.2022.840965840965Artificial intelligence assisted compositional analyses of human abdominal aortic aneurysms ex vivoBjarne Thorsted0Lisette Bjerregaard1Pia S. Jensen2Pia S. Jensen3Pia S. Jensen4Lars M. Rasmussen5Lars M. Rasmussen6Lars M. Rasmussen7Jes S. Lindholt8Jes S. Lindholt9Maria Bloksgaard10Department of Cardiothoracic and Vascular Surgery, Odense University Hospital, Odense, DenmarkDepartment of Cardiothoracic and Vascular Surgery, Odense University Hospital, Odense, DenmarkDepartment of Clinical Biochemistry and Pharmacology, Odense University Hospital, Odense, DenmarkOdense Artery Biobank, Odense University Hospital, Odense, DenmarkCenter for Individualized Medicine in Arterial Diseases, Odense University Hospital, Odense, DenmarkDepartment of Clinical Biochemistry and Pharmacology, Odense University Hospital, Odense, DenmarkOdense Artery Biobank, Odense University Hospital, Odense, DenmarkCenter for Individualized Medicine in Arterial Diseases, Odense University Hospital, Odense, DenmarkDepartment of Cardiothoracic and Vascular Surgery, Odense University Hospital, Odense, DenmarkCenter for Individualized Medicine in Arterial Diseases, Odense University Hospital, Odense, DenmarkMedical Molecular Pharmacology Laboratory, Cardiovascular and Renal Research Unit, Department of Molecular Medicine, University of Southern Denmark, Odense, DenmarkQuantification of histological information from excised human abdominal aortic aneurysm (AAA) specimens may provide essential information on the degree of infiltration of inflammatory cells in different regions of the AAA. Such information will support mechanistic insight in AAA pathology and can be linked to clinical measures for further development of AAA treatment regimens. We hypothesize that artificial intelligence can support high throughput analyses of histological sections of excised human AAA. We present an analysis framework based on supervised machine learning. We used TensorFlow and QuPath to determine the overall architecture of the AAA: thrombus, arterial wall, and adventitial loose connective tissue. Within the wall and adventitial zones, the content of collagen, elastin, and specific inflammatory cells was quantified. A deep neural network (DNN) was trained on manually annotated, Weigert stained, tissue sections (14 patients) and validated on images from two other patients. Finally, we applied the method on 95 new patient samples. The DNN was able to segment the sections according to the overall wall architecture with Jaccard coefficients after 65 epocs of 92% for the training and 88% for the validation data set, respectively. Precision and recall both reached 92%. The zone areas were highly variable between patients, as were the outputs on total cell count and elastin/collagen fiber content. The number of specific cells or stained area per zone was deterministically determined. However, combining the masks based on the Weigert stainings, with images of immunostained serial sections requires addition of landmark recognition to the analysis path. The combination of digital pathology, the DNN we developed, and landmark registration will provide a strong tool for future analyses of the histology of excised human AAA. In combination with biomechanical testing and microstructurally motivated mathematical models of AAA remodeling, the method has the potential to be a strong tool to provide mechanistic insight in the disease. In combination with each patients’ demographic and clinical profile, the method can be an interesting tool to in supportof a better treatment regime for the patients.https://www.frontiersin.org/articles/10.3389/fphys.2022.840965/fullabdominal aortic aneurysmneural networkautomated histological image analysismachine learningcell detection and segmentationextracellular matrix |
| spellingShingle | Bjarne Thorsted Lisette Bjerregaard Pia S. Jensen Pia S. Jensen Pia S. Jensen Lars M. Rasmussen Lars M. Rasmussen Lars M. Rasmussen Jes S. Lindholt Jes S. Lindholt Maria Bloksgaard Artificial intelligence assisted compositional analyses of human abdominal aortic aneurysms ex vivo Frontiers in Physiology abdominal aortic aneurysm neural network automated histological image analysis machine learning cell detection and segmentation extracellular matrix |
| title | Artificial intelligence assisted compositional analyses of human abdominal aortic aneurysms ex vivo |
| title_full | Artificial intelligence assisted compositional analyses of human abdominal aortic aneurysms ex vivo |
| title_fullStr | Artificial intelligence assisted compositional analyses of human abdominal aortic aneurysms ex vivo |
| title_full_unstemmed | Artificial intelligence assisted compositional analyses of human abdominal aortic aneurysms ex vivo |
| title_short | Artificial intelligence assisted compositional analyses of human abdominal aortic aneurysms ex vivo |
| title_sort | artificial intelligence assisted compositional analyses of human abdominal aortic aneurysms ex vivo |
| topic | abdominal aortic aneurysm neural network automated histological image analysis machine learning cell detection and segmentation extracellular matrix |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2022.840965/full |
| work_keys_str_mv | AT bjarnethorsted artificialintelligenceassistedcompositionalanalysesofhumanabdominalaorticaneurysmsexvivo AT lisettebjerregaard artificialintelligenceassistedcompositionalanalysesofhumanabdominalaorticaneurysmsexvivo AT piasjensen artificialintelligenceassistedcompositionalanalysesofhumanabdominalaorticaneurysmsexvivo AT piasjensen artificialintelligenceassistedcompositionalanalysesofhumanabdominalaorticaneurysmsexvivo AT piasjensen artificialintelligenceassistedcompositionalanalysesofhumanabdominalaorticaneurysmsexvivo AT larsmrasmussen artificialintelligenceassistedcompositionalanalysesofhumanabdominalaorticaneurysmsexvivo AT larsmrasmussen artificialintelligenceassistedcompositionalanalysesofhumanabdominalaorticaneurysmsexvivo AT larsmrasmussen artificialintelligenceassistedcompositionalanalysesofhumanabdominalaorticaneurysmsexvivo AT jesslindholt artificialintelligenceassistedcompositionalanalysesofhumanabdominalaorticaneurysmsexvivo AT jesslindholt artificialintelligenceassistedcompositionalanalysesofhumanabdominalaorticaneurysmsexvivo AT mariabloksgaard artificialintelligenceassistedcompositionalanalysesofhumanabdominalaorticaneurysmsexvivo |