Abstract
Renal biopsies form the gold standard of diagnostic and prognostic assessments of renal transplants. With the addition of new quantitative strategies to supplement renal biopsy interpretation such as gene array and metabolomics, the capability to incorporate all quantitative measures for clinical interpretation will require multi-dimensional analyses. Currently, renal biopsies are analyzed manually; the quantitative features of pathology observed on the biopsies are limited to hand counts. Standardized, automated detection of pathology observed in a kidney transplant biopsy will enable the input of these digital images alongside other quantitative measures of new technologies, with potential gains in precision in patient care. We investigate a learning framework to detect pathological changes in biopsy image that addresses two main issues: the inadequate training set and the significant diversity of color and tissue shape on whole slide images. Two case studies, automatic detection of interstitial inflammation and tubular cast, are presented in this work. Afterwards, we propose a fully automated glomerulus extraction framework on micrograph of entire renal tissue, focusing on extracting Bowman's capsule, the supportive structure of glomeruli. Statistical approaches are also introduced to further improve the performance. Human expert annotations of interstitial inflammation and tubular casts in 10 H&E stained renal tissues of nonhuman primates and more than 100 glomeruli are used to demonstrate the superior performance of the proposed algorithm over existing solutions.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the 2016 IEEE 12th International Conference on e-Science, e-Science 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 165-174 |
| Number of pages | 10 |
| ISBN (Electronic) | 9781509042722 |
| DOIs | |
| State | Published - Mar 3 2017 |
| Event | 12th IEEE International Conference on e-Science, e-Science 2016 - Baltimore, United States Duration: Oct 23 2016 → Oct 27 2016 |
Other
| Other | 12th IEEE International Conference on e-Science, e-Science 2016 |
|---|---|
| Country | United States |
| City | Baltimore |
| Period | 10/23/16 → 10/27/16 |
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ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
- Environmental Science (miscellaneous)
- Medicine (miscellaneous)
- Social Sciences (miscellaneous)
- Agricultural and Biological Sciences (miscellaneous)
- Computer Science Applications
Cite this
Automatic glomerulus extraction in whole slide images towards computer aided diagnosis. / Zhao, Yan; Black, Edgar F.; Marini, Luigi; McHenry, Kenton; Kenyon, Norma S; Patil, Rachana; Balla, Andre; Bartholomew, Amelia.
Proceedings of the 2016 IEEE 12th International Conference on e-Science, e-Science 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 165-174 7870897.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Automatic glomerulus extraction in whole slide images towards computer aided diagnosis
AU - Zhao, Yan
AU - Black, Edgar F.
AU - Marini, Luigi
AU - McHenry, Kenton
AU - Kenyon, Norma S
AU - Patil, Rachana
AU - Balla, Andre
AU - Bartholomew, Amelia
PY - 2017/3/3
Y1 - 2017/3/3
N2 - Renal biopsies form the gold standard of diagnostic and prognostic assessments of renal transplants. With the addition of new quantitative strategies to supplement renal biopsy interpretation such as gene array and metabolomics, the capability to incorporate all quantitative measures for clinical interpretation will require multi-dimensional analyses. Currently, renal biopsies are analyzed manually; the quantitative features of pathology observed on the biopsies are limited to hand counts. Standardized, automated detection of pathology observed in a kidney transplant biopsy will enable the input of these digital images alongside other quantitative measures of new technologies, with potential gains in precision in patient care. We investigate a learning framework to detect pathological changes in biopsy image that addresses two main issues: the inadequate training set and the significant diversity of color and tissue shape on whole slide images. Two case studies, automatic detection of interstitial inflammation and tubular cast, are presented in this work. Afterwards, we propose a fully automated glomerulus extraction framework on micrograph of entire renal tissue, focusing on extracting Bowman's capsule, the supportive structure of glomeruli. Statistical approaches are also introduced to further improve the performance. Human expert annotations of interstitial inflammation and tubular casts in 10 H&E stained renal tissues of nonhuman primates and more than 100 glomeruli are used to demonstrate the superior performance of the proposed algorithm over existing solutions.
AB - Renal biopsies form the gold standard of diagnostic and prognostic assessments of renal transplants. With the addition of new quantitative strategies to supplement renal biopsy interpretation such as gene array and metabolomics, the capability to incorporate all quantitative measures for clinical interpretation will require multi-dimensional analyses. Currently, renal biopsies are analyzed manually; the quantitative features of pathology observed on the biopsies are limited to hand counts. Standardized, automated detection of pathology observed in a kidney transplant biopsy will enable the input of these digital images alongside other quantitative measures of new technologies, with potential gains in precision in patient care. We investigate a learning framework to detect pathological changes in biopsy image that addresses two main issues: the inadequate training set and the significant diversity of color and tissue shape on whole slide images. Two case studies, automatic detection of interstitial inflammation and tubular cast, are presented in this work. Afterwards, we propose a fully automated glomerulus extraction framework on micrograph of entire renal tissue, focusing on extracting Bowman's capsule, the supportive structure of glomeruli. Statistical approaches are also introduced to further improve the performance. Human expert annotations of interstitial inflammation and tubular casts in 10 H&E stained renal tissues of nonhuman primates and more than 100 glomeruli are used to demonstrate the superior performance of the proposed algorithm over existing solutions.
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U2 - 10.1109/eScience.2016.7870897
DO - 10.1109/eScience.2016.7870897
M3 - Conference contribution
AN - SCOPUS:85016756559
SP - 165
EP - 174
BT - Proceedings of the 2016 IEEE 12th International Conference on e-Science, e-Science 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -