Computational Pipeline for Glomerular Segmentation and Association of the Quantified Regions with Prognosis of Kidney Function in IgA Nephropathy
The histopathological findings of the glomeruli from whole slide images (WSIs) of a renal biopsy play an important role in diagnosing and grading kidney disease. This study aimed to develop an automated computational pipeline to detect glomeruli and to segment the histopathological regions inside of...
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MDPI AG
2022-11-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/12/12/2955 |
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| author | Yoshimasa Kawazoe Kiminori Shimamoto Ryohei Yamaguchi Issei Nakamura Kota Yoneda Emiko Shinohara Yukako Shintani-Domoto Tetsuo Ushiku Tatsuo Tsukamoto Kazuhiko Ohe |
| author_facet | Yoshimasa Kawazoe Kiminori Shimamoto Ryohei Yamaguchi Issei Nakamura Kota Yoneda Emiko Shinohara Yukako Shintani-Domoto Tetsuo Ushiku Tatsuo Tsukamoto Kazuhiko Ohe |
| author_sort | Yoshimasa Kawazoe |
| collection | DOAJ |
| description | The histopathological findings of the glomeruli from whole slide images (WSIs) of a renal biopsy play an important role in diagnosing and grading kidney disease. This study aimed to develop an automated computational pipeline to detect glomeruli and to segment the histopathological regions inside of the glomerulus in a WSI. In order to assess the significance of this pipeline, we conducted a multivariate regression analysis to determine whether the quantified regions were associated with the prognosis of kidney function in 46 cases of immunoglobulin A nephropathy (IgAN). The developed pipelines showed a mean intersection over union (IoU) of 0.670 and 0.693 for five classes (i.e., background, Bowman’s space, glomerular tuft, crescentic, and sclerotic regions) against the WSI of its facility, and 0.678 and 0.609 against the WSI of the external facility. The multivariate analysis revealed that the predicted sclerotic regions, even those that were predicted by the external model, had a significant negative impact on the slope of the estimated glomerular filtration rate after biopsy. This is the first study to demonstrate that the quantified sclerotic regions that are predicted by an automated computational pipeline for the segmentation of the histopathological glomerular components on WSIs impact the prognosis of kidney function in patients with IgAN. |
| first_indexed | 2024-03-09T17:08:24Z |
| format | Article |
| id | doaj.art-75d774895b824436bd704ac63ebd313b |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T17:08:24Z |
| publishDate | 2022-11-01 |
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| series | Diagnostics |
| spelling | doaj.art-75d774895b824436bd704ac63ebd313b2023-11-24T14:16:10ZengMDPI AGDiagnostics2075-44182022-11-011212295510.3390/diagnostics12122955Computational Pipeline for Glomerular Segmentation and Association of the Quantified Regions with Prognosis of Kidney Function in IgA NephropathyYoshimasa Kawazoe0Kiminori Shimamoto1Ryohei Yamaguchi2Issei Nakamura3Kota Yoneda4Emiko Shinohara5Yukako Shintani-Domoto6Tetsuo Ushiku7Tatsuo Tsukamoto8Kazuhiko Ohe9Artificial Intelligence in Healthcare, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-0033, JapanArtificial Intelligence in Healthcare, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-0033, JapanOhshima Memorial Kisen Hospital, 3-5-15, Misaki, Chiba 274-0812, JapanNTT DOCOMO, Inc., Sanno Park Tower, 2-11-1, Nagata-cho, Chiyoda-ku, Tokyo 100-6150, JapanDepartment of Reproductive, Developmental, and Aging Sciences, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-0033, JapanArtificial Intelligence in Healthcare, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-0033, JapanDepartment of Diagnostic Pathology, Nippon Medical School Hospital, 1-1-5, Sendagi, Bunkyo-ku, Tokyo 113-8602, JapanDepartment of Pathology, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-0033, JapanDepartment of Nephrology and Dialysis, Tazuke Kofukai Medical Research Institute, Kitano Hospital, 2-4-20, Ohgimachi, Kita-ku, Osaka 530-8480, JapanDepartment of Biomedical Informatics, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-0033, JapanThe histopathological findings of the glomeruli from whole slide images (WSIs) of a renal biopsy play an important role in diagnosing and grading kidney disease. This study aimed to develop an automated computational pipeline to detect glomeruli and to segment the histopathological regions inside of the glomerulus in a WSI. In order to assess the significance of this pipeline, we conducted a multivariate regression analysis to determine whether the quantified regions were associated with the prognosis of kidney function in 46 cases of immunoglobulin A nephropathy (IgAN). The developed pipelines showed a mean intersection over union (IoU) of 0.670 and 0.693 for five classes (i.e., background, Bowman’s space, glomerular tuft, crescentic, and sclerotic regions) against the WSI of its facility, and 0.678 and 0.609 against the WSI of the external facility. The multivariate analysis revealed that the predicted sclerotic regions, even those that were predicted by the external model, had a significant negative impact on the slope of the estimated glomerular filtration rate after biopsy. This is the first study to demonstrate that the quantified sclerotic regions that are predicted by an automated computational pipeline for the segmentation of the histopathological glomerular components on WSIs impact the prognosis of kidney function in patients with IgAN.https://www.mdpi.com/2075-4418/12/12/2955computer visiondeep learningdigital pathologywhole slide imaging (WSI)object detectionsegmentation |
| spellingShingle | Yoshimasa Kawazoe Kiminori Shimamoto Ryohei Yamaguchi Issei Nakamura Kota Yoneda Emiko Shinohara Yukako Shintani-Domoto Tetsuo Ushiku Tatsuo Tsukamoto Kazuhiko Ohe Computational Pipeline for Glomerular Segmentation and Association of the Quantified Regions with Prognosis of Kidney Function in IgA Nephropathy Diagnostics computer vision deep learning digital pathology whole slide imaging (WSI) object detection segmentation |
| title | Computational Pipeline for Glomerular Segmentation and Association of the Quantified Regions with Prognosis of Kidney Function in IgA Nephropathy |
| title_full | Computational Pipeline for Glomerular Segmentation and Association of the Quantified Regions with Prognosis of Kidney Function in IgA Nephropathy |
| title_fullStr | Computational Pipeline for Glomerular Segmentation and Association of the Quantified Regions with Prognosis of Kidney Function in IgA Nephropathy |
| title_full_unstemmed | Computational Pipeline for Glomerular Segmentation and Association of the Quantified Regions with Prognosis of Kidney Function in IgA Nephropathy |
| title_short | Computational Pipeline for Glomerular Segmentation and Association of the Quantified Regions with Prognosis of Kidney Function in IgA Nephropathy |
| title_sort | computational pipeline for glomerular segmentation and association of the quantified regions with prognosis of kidney function in iga nephropathy |
| topic | computer vision deep learning digital pathology whole slide imaging (WSI) object detection segmentation |
| url | https://www.mdpi.com/2075-4418/12/12/2955 |
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