Machine Learning Quantification of Amyloid Deposits in Histological Images of Ligamentum Flavum
Wild-type transthyretin amyloidosis (ATTRwt) is an underdiagnosed and potentially fatal disease. Interestingly, ATTRwt deposits have been found to deposit in the ligamentum flavum (LF) of patients with lumbar spinal stenosis before the development of systemic and cardiac amyloidosis. In order to stu...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-01-01
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| Series: | Journal of Pathology Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S215335392200013X |
| _version_ | 1797976970107027456 |
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| author | Andy Y. Wang Vaishnavi Sharma Harleen Saini Joseph N. Tingen Alexandra Flores Diang Liu Mina G. Safain James Kryzanski Ellen D. McPhail Knarik Arkun Ron I. Riesenburger |
| author_facet | Andy Y. Wang Vaishnavi Sharma Harleen Saini Joseph N. Tingen Alexandra Flores Diang Liu Mina G. Safain James Kryzanski Ellen D. McPhail Knarik Arkun Ron I. Riesenburger |
| author_sort | Andy Y. Wang |
| collection | DOAJ |
| description | Wild-type transthyretin amyloidosis (ATTRwt) is an underdiagnosed and potentially fatal disease. Interestingly, ATTRwt deposits have been found to deposit in the ligamentum flavum (LF) of patients with lumbar spinal stenosis before the development of systemic and cardiac amyloidosis. In order to study this phenomenon and its possible relationship with LF thickening and systemic amyloidosis, a precise method of quantifying amyloid deposits in histological slides of LF is critical. However, such a method is currently unavailable. Here, we present a machine learning quantification method with Trainable Weka Segmentation (TWS) to assess amyloid deposition in histological slides of LF. Images of ligamentum flavum specimens stained with Congo red are obtained from spinal stenosis patients undergoing laminectomies and confirmed to be positive for ATTRwt. Amyloid deposits in these specimens are classified and quantified by TWS through training the algorithm via user-directed annotations on images of LF. TWS can also be automated through exposure to a set of training images with user-directed annotations, and then applied] to a set of new images without additional annotations. Additional methods of color thresholding and manual segmentation are also used on these images for comparison to TWS. We develop the use of TWS in images of LF and demonstrate its potential for automated quantification. TWS is strongly correlated with manual segmentation in the training set of images with user-directed annotations (R = 0.98; p = 0.0033) as well as in the application set of images where TWS was automated (R = 0.94; p = 0.016). Color thresholding was weakly correlated with manual segmentation in the training set of images (R = 0.78; p = 0.12) and in the application set of images (R = 0.65; p = 0.23). TWS machine learning closely correlates with the gold-standard comparator of manual segmentation and outperforms the color thresholding method. This novel machine learning method to quantify amyloid deposition in histological slides of ligamentum flavum is a precise, objective, accessible, high throughput, and powerful tool that will hopefully pave the way towards future research and clinical applications. |
| first_indexed | 2024-04-11T04:59:29Z |
| format | Article |
| id | doaj.art-183a8d3110f142f088e3a9553fa3510c |
| institution | Directory Open Access Journal |
| issn | 2153-3539 |
| language | English |
| last_indexed | 2024-04-11T04:59:29Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pathology Informatics |
| spelling | doaj.art-183a8d3110f142f088e3a9553fa3510c2022-12-26T04:07:59ZengElsevierJournal of Pathology Informatics2153-35392022-01-0113100013Machine Learning Quantification of Amyloid Deposits in Histological Images of Ligamentum FlavumAndy Y. Wang0Vaishnavi Sharma1Harleen Saini2Joseph N. Tingen3Alexandra Flores4Diang Liu5Mina G. Safain6James Kryzanski7Ellen D. McPhail8Knarik Arkun9Ron I. Riesenburger10Department of Neurosurgery, Tufts Medical Center, Boston, Massachusetts, USADepartment of Neurosurgery, Tufts Medical Center, Boston, Massachusetts, USADepartment of Neurosurgery, Tufts Medical Center, Boston, Massachusetts, USADepartment of Neurosurgery, Tufts Medical Center, Boston, Massachusetts, USADepartment of Neurosurgery, Tufts Medical Center, Boston, Massachusetts, USADepartment of Neurosurgery, Tufts Medical Center, Boston, Massachusetts, USADepartment of Neurosurgery, Tufts Medical Center, Boston, Massachusetts, USADepartment of Neurosurgery, Tufts Medical Center, Boston, Massachusetts, USADepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USADepartment of Pathology and Laboratory Medicine, Tufts Medical Center, Boston, Massachusetts, USA; Department of Neurosurgery, Tufts Medical Center, Boston, Massachusetts, USADepartment of Neurosurgery, Tufts Medical Center, Boston, Massachusetts, USA; Corresponding author at: Department of Neurosurgery, Tufts Medical Center, 800 Washington St, Boston, MA 02111, USAWild-type transthyretin amyloidosis (ATTRwt) is an underdiagnosed and potentially fatal disease. Interestingly, ATTRwt deposits have been found to deposit in the ligamentum flavum (LF) of patients with lumbar spinal stenosis before the development of systemic and cardiac amyloidosis. In order to study this phenomenon and its possible relationship with LF thickening and systemic amyloidosis, a precise method of quantifying amyloid deposits in histological slides of LF is critical. However, such a method is currently unavailable. Here, we present a machine learning quantification method with Trainable Weka Segmentation (TWS) to assess amyloid deposition in histological slides of LF. Images of ligamentum flavum specimens stained with Congo red are obtained from spinal stenosis patients undergoing laminectomies and confirmed to be positive for ATTRwt. Amyloid deposits in these specimens are classified and quantified by TWS through training the algorithm via user-directed annotations on images of LF. TWS can also be automated through exposure to a set of training images with user-directed annotations, and then applied] to a set of new images without additional annotations. Additional methods of color thresholding and manual segmentation are also used on these images for comparison to TWS. We develop the use of TWS in images of LF and demonstrate its potential for automated quantification. TWS is strongly correlated with manual segmentation in the training set of images with user-directed annotations (R = 0.98; p = 0.0033) as well as in the application set of images where TWS was automated (R = 0.94; p = 0.016). Color thresholding was weakly correlated with manual segmentation in the training set of images (R = 0.78; p = 0.12) and in the application set of images (R = 0.65; p = 0.23). TWS machine learning closely correlates with the gold-standard comparator of manual segmentation and outperforms the color thresholding method. This novel machine learning method to quantify amyloid deposition in histological slides of ligamentum flavum is a precise, objective, accessible, high throughput, and powerful tool that will hopefully pave the way towards future research and clinical applications.http://www.sciencedirect.com/science/article/pii/S215335392200013XWild-type transthyretin amyloidLigamentum flavumTrainable Weka SegmentationMachine learningColor thresholding |
| spellingShingle | Andy Y. Wang Vaishnavi Sharma Harleen Saini Joseph N. Tingen Alexandra Flores Diang Liu Mina G. Safain James Kryzanski Ellen D. McPhail Knarik Arkun Ron I. Riesenburger Machine Learning Quantification of Amyloid Deposits in Histological Images of Ligamentum Flavum Journal of Pathology Informatics Wild-type transthyretin amyloid Ligamentum flavum Trainable Weka Segmentation Machine learning Color thresholding |
| title | Machine Learning Quantification of Amyloid Deposits in Histological Images of Ligamentum Flavum |
| title_full | Machine Learning Quantification of Amyloid Deposits in Histological Images of Ligamentum Flavum |
| title_fullStr | Machine Learning Quantification of Amyloid Deposits in Histological Images of Ligamentum Flavum |
| title_full_unstemmed | Machine Learning Quantification of Amyloid Deposits in Histological Images of Ligamentum Flavum |
| title_short | Machine Learning Quantification of Amyloid Deposits in Histological Images of Ligamentum Flavum |
| title_sort | machine learning quantification of amyloid deposits in histological images of ligamentum flavum |
| topic | Wild-type transthyretin amyloid Ligamentum flavum Trainable Weka Segmentation Machine learning Color thresholding |
| url | http://www.sciencedirect.com/science/article/pii/S215335392200013X |
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