Computer-based image analysis of liver steatosis with large-scale microscopy imagery and correlation with magnetic resonance imaging lipid analysis

Jun Kong; Michael J. Lee; Pelin Bagci; Puneet Sharma; Diego Martin; N. Volkan Adsay; Joel H. Saltz; Alton B. Farris

doi:10.1109/BIBM.2011.37

Computer-based image analysis of liver steatosis with large-scale microscopy imagery and correlation with magnetic resonance imaging lipid analysis

Jun Kong, Michael J. Lee, Pelin Bagci, Puneet Sharma, Diego Martin, N. Volkan Adsay, Joel H. Saltz, Alton B. Farris

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

16 Scopus citations

Abstract

Most pathology analyses and measurements are prevalently carried out by trained reviewers in both clinical and research settings. Therefore, the resulting outputs are inexorably biased by interpreters and degraded with poor reproducibility. In this paper, we propose a computerized image analysis paradigm enabling quantitative characterizations of steatosis areas in microscopy images of pediatric liver biopsies. With the same set of patients, we also acquired the lipid measurements from magnetic resonance imaging data analysis for correlation investigation. Our preliminary results suggest a high correlation between the steatosis areas quantized with microscopy images and the lipid percentages calculated from radiology imaging data. Additionally, we compared the performance of the proposed analysis method with those of three certified pathologists and a popular commercial algorithm. The results suggest the superiority of our method to both human reviewers and the commercial method in terms of the steatosis-lipid correlation strength. This demonstrates that the developed method is promising for generating quantitative and reliable analysis results to better support further liver disease study.

Original language	English (US)
Title of host publication	Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011
Pages	333-338
Number of pages	6
DOIs	https://doi.org/10.1109/BIBM.2011.37
State	Published - 2011
Event	2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011 - Atlanta, GA, United States Duration: Nov 12 2011 → Nov 15 2011

Publication series

Name	Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011

Other

Other	2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011
Country/Territory	United States
City	Atlanta, GA
Period	11/12/11 → 11/15/11

Keywords

Data correlation
Large-scale microscopy image analysis
Liver steatosis quantification
Parallel computation
Tissue representation

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics
Health Information Management

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10.1109/BIBM.2011.37

Cite this

Kong, J., Lee, M. J., Bagci, P., Sharma, P., Martin, D., Adsay, N. V., Saltz, J. H., & Farris, A. B. (2011). Computer-based image analysis of liver steatosis with large-scale microscopy imagery and correlation with magnetic resonance imaging lipid analysis. In Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011 (pp. 333-338). Article 6120462 (Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011). https://doi.org/10.1109/BIBM.2011.37

Computer-based image analysis of liver steatosis with large-scale microscopy imagery and correlation with magnetic resonance imaging lipid analysis. / Kong, Jun; Lee, Michael J.; Bagci, Pelin et al.
Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011. 2011. p. 333-338 6120462 (Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kong, J, Lee, MJ, Bagci, P, Sharma, P, Martin, D, Adsay, NV, Saltz, JH & Farris, AB 2011, Computer-based image analysis of liver steatosis with large-scale microscopy imagery and correlation with magnetic resonance imaging lipid analysis. in Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011., 6120462, Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011, pp. 333-338, 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011, Atlanta, GA, United States, 11/12/11. https://doi.org/10.1109/BIBM.2011.37

Kong J, Lee MJ, Bagci P, Sharma P, Martin D, Adsay NV et al. Computer-based image analysis of liver steatosis with large-scale microscopy imagery and correlation with magnetic resonance imaging lipid analysis. In Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011. 2011. p. 333-338. 6120462. (Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011). doi: 10.1109/BIBM.2011.37

Kong, Jun ; Lee, Michael J. ; Bagci, Pelin et al. / Computer-based image analysis of liver steatosis with large-scale microscopy imagery and correlation with magnetic resonance imaging lipid analysis. Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011. 2011. pp. 333-338 (Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011).

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abstract = "Most pathology analyses and measurements are prevalently carried out by trained reviewers in both clinical and research settings. Therefore, the resulting outputs are inexorably biased by interpreters and degraded with poor reproducibility. In this paper, we propose a computerized image analysis paradigm enabling quantitative characterizations of steatosis areas in microscopy images of pediatric liver biopsies. With the same set of patients, we also acquired the lipid measurements from magnetic resonance imaging data analysis for correlation investigation. Our preliminary results suggest a high correlation between the steatosis areas quantized with microscopy images and the lipid percentages calculated from radiology imaging data. Additionally, we compared the performance of the proposed analysis method with those of three certified pathologists and a popular commercial algorithm. The results suggest the superiority of our method to both human reviewers and the commercial method in terms of the steatosis-lipid correlation strength. This demonstrates that the developed method is promising for generating quantitative and reliable analysis results to better support further liver disease study.",

keywords = "Data correlation, Large-scale microscopy image analysis, Liver steatosis quantification, Parallel computation, Tissue representation",

author = "Jun Kong and Lee, \{Michael J.\} and Pelin Bagci and Puneet Sharma and Diego Martin and Adsay, \{N. Volkan\} and Saltz, \{Joel H.\} and Farris, \{Alton B.\}",

year = "2011",

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AU - Bagci, Pelin

AU - Sharma, Puneet

AU - Martin, Diego

AU - Adsay, N. Volkan

AU - Saltz, Joel H.

AU - Farris, Alton B.

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N2 - Most pathology analyses and measurements are prevalently carried out by trained reviewers in both clinical and research settings. Therefore, the resulting outputs are inexorably biased by interpreters and degraded with poor reproducibility. In this paper, we propose a computerized image analysis paradigm enabling quantitative characterizations of steatosis areas in microscopy images of pediatric liver biopsies. With the same set of patients, we also acquired the lipid measurements from magnetic resonance imaging data analysis for correlation investigation. Our preliminary results suggest a high correlation between the steatosis areas quantized with microscopy images and the lipid percentages calculated from radiology imaging data. Additionally, we compared the performance of the proposed analysis method with those of three certified pathologists and a popular commercial algorithm. The results suggest the superiority of our method to both human reviewers and the commercial method in terms of the steatosis-lipid correlation strength. This demonstrates that the developed method is promising for generating quantitative and reliable analysis results to better support further liver disease study.

AB - Most pathology analyses and measurements are prevalently carried out by trained reviewers in both clinical and research settings. Therefore, the resulting outputs are inexorably biased by interpreters and degraded with poor reproducibility. In this paper, we propose a computerized image analysis paradigm enabling quantitative characterizations of steatosis areas in microscopy images of pediatric liver biopsies. With the same set of patients, we also acquired the lipid measurements from magnetic resonance imaging data analysis for correlation investigation. Our preliminary results suggest a high correlation between the steatosis areas quantized with microscopy images and the lipid percentages calculated from radiology imaging data. Additionally, we compared the performance of the proposed analysis method with those of three certified pathologists and a popular commercial algorithm. The results suggest the superiority of our method to both human reviewers and the commercial method in terms of the steatosis-lipid correlation strength. This demonstrates that the developed method is promising for generating quantitative and reliable analysis results to better support further liver disease study.

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KW - Parallel computation

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Computer-based image analysis of liver steatosis with large-scale microscopy imagery and correlation with magnetic resonance imaging lipid analysis

Abstract

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