Experimenting Liver Fibrosis Diagnostic by Two Photon Excitation Microscopy and Bag-of-Features Image Classification
The accurate staging of liver fibrosis is of paramount importance to determine the state of disease progression, therapy responses, and to optimize disease treatment strategies. Non-linear optical microscopy techniques such as two-photon excitation fluorescence (TPEF) and second harmonic generation...
| Main Authors: | , , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2016
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/82169 http://hdl.handle.net/10220/41141 |
| _version_ | 1824456916243316736 |
|---|---|
| author | Stanciu, Stefan G. Xu, Shuoyu Peng, Qiwen Yan, Jie Stanciu, George A. Welsch, Roy E. So, Peter T. C. Csucs, Gabor Yu, Hanry |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Stanciu, Stefan G. Xu, Shuoyu Peng, Qiwen Yan, Jie Stanciu, George A. Welsch, Roy E. So, Peter T. C. Csucs, Gabor Yu, Hanry |
| author_sort | Stanciu, Stefan G. |
| collection | NTU |
| description | The accurate staging of liver fibrosis is of paramount importance to determine the state of disease progression, therapy responses, and to optimize disease treatment strategies. Non-linear optical microscopy techniques such as two-photon excitation fluorescence (TPEF) and second harmonic generation (SHG) can image the endogenous signals of tissue structures and can be used for fibrosis assessment on non-stained tissue samples. While image analysis of collagen in SHG images was consistently addressed until now, cellular and tissue information included in TPEF images, such as inflammatory and hepatic cell damage, equally important as collagen deposition imaged by SHG, remain poorly exploited to date. We address this situation by experimenting liver fibrosis quantification and scoring using a combined approach based on TPEF liver surface imaging on a Thioacetamide-induced rat model and a gradient based Bag-of-Features (BoF) image classification strategy. We report the assessed performance results and discuss the influence of specific BoF parameters to the performance of the fibrosis scoring framework. |
| first_indexed | 2025-02-19T04:01:42Z |
| format | Journal Article |
| id | ntu-10356/82169 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T04:01:42Z |
| publishDate | 2016 |
| record_format | dspace |
| spelling | ntu-10356/821692022-02-16T16:28:46Z Experimenting Liver Fibrosis Diagnostic by Two Photon Excitation Microscopy and Bag-of-Features Image Classification Stanciu, Stefan G. Xu, Shuoyu Peng, Qiwen Yan, Jie Stanciu, George A. Welsch, Roy E. So, Peter T. C. Csucs, Gabor Yu, Hanry School of Computer Engineering Bioinformatics Research Centre Biomedical engineering Computer science The accurate staging of liver fibrosis is of paramount importance to determine the state of disease progression, therapy responses, and to optimize disease treatment strategies. Non-linear optical microscopy techniques such as two-photon excitation fluorescence (TPEF) and second harmonic generation (SHG) can image the endogenous signals of tissue structures and can be used for fibrosis assessment on non-stained tissue samples. While image analysis of collagen in SHG images was consistently addressed until now, cellular and tissue information included in TPEF images, such as inflammatory and hepatic cell damage, equally important as collagen deposition imaged by SHG, remain poorly exploited to date. We address this situation by experimenting liver fibrosis quantification and scoring using a combined approach based on TPEF liver surface imaging on a Thioacetamide-induced rat model and a gradient based Bag-of-Features (BoF) image classification strategy. We report the assessed performance results and discuss the influence of specific BoF parameters to the performance of the fibrosis scoring framework. ASTAR (Agency for Sci., Tech. and Research, S’pore) Published version 2016-08-16T08:22:15Z 2019-12-06T14:47:56Z 2016-08-16T08:22:15Z 2019-12-06T14:47:56Z 2014 Journal Article Stanciu, S. G., Xu, S., Peng, Q., Yan, J., Stanciu, G. A., Welsch, R. E., et al. (2014). Experimenting Liver Fibrosis Diagnostic by Two Photon Excitation Microscopy and Bag-of-Features Image Classification. Scientific Reports, 4, 4636-. 2045-2322 https://hdl.handle.net/10356/82169 http://hdl.handle.net/10220/41141 10.1038/srep04636 24717650 en Scientific Reports This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported license. The images in this article are included in the article's Creative Commons license, unless indicated otherwise in the image credit; if the image is not included under the Creative Commons license, users will need to obtain permission from the license holder in order to reproduce the image. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/ application/pdf |
| spellingShingle | Biomedical engineering Computer science Stanciu, Stefan G. Xu, Shuoyu Peng, Qiwen Yan, Jie Stanciu, George A. Welsch, Roy E. So, Peter T. C. Csucs, Gabor Yu, Hanry Experimenting Liver Fibrosis Diagnostic by Two Photon Excitation Microscopy and Bag-of-Features Image Classification |
| title | Experimenting Liver Fibrosis Diagnostic by Two Photon Excitation Microscopy and Bag-of-Features Image Classification |
| title_full | Experimenting Liver Fibrosis Diagnostic by Two Photon Excitation Microscopy and Bag-of-Features Image Classification |
| title_fullStr | Experimenting Liver Fibrosis Diagnostic by Two Photon Excitation Microscopy and Bag-of-Features Image Classification |
| title_full_unstemmed | Experimenting Liver Fibrosis Diagnostic by Two Photon Excitation Microscopy and Bag-of-Features Image Classification |
| title_short | Experimenting Liver Fibrosis Diagnostic by Two Photon Excitation Microscopy and Bag-of-Features Image Classification |
| title_sort | experimenting liver fibrosis diagnostic by two photon excitation microscopy and bag of features image classification |
| topic | Biomedical engineering Computer science |
| url | https://hdl.handle.net/10356/82169 http://hdl.handle.net/10220/41141 |
| work_keys_str_mv | AT stanciustefang experimentingliverfibrosisdiagnosticbytwophotonexcitationmicroscopyandbagoffeaturesimageclassification AT xushuoyu experimentingliverfibrosisdiagnosticbytwophotonexcitationmicroscopyandbagoffeaturesimageclassification AT pengqiwen experimentingliverfibrosisdiagnosticbytwophotonexcitationmicroscopyandbagoffeaturesimageclassification AT yanjie experimentingliverfibrosisdiagnosticbytwophotonexcitationmicroscopyandbagoffeaturesimageclassification AT stanciugeorgea experimentingliverfibrosisdiagnosticbytwophotonexcitationmicroscopyandbagoffeaturesimageclassification AT welschroye experimentingliverfibrosisdiagnosticbytwophotonexcitationmicroscopyandbagoffeaturesimageclassification AT sopetertc experimentingliverfibrosisdiagnosticbytwophotonexcitationmicroscopyandbagoffeaturesimageclassification AT csucsgabor experimentingliverfibrosisdiagnosticbytwophotonexcitationmicroscopyandbagoffeaturesimageclassification AT yuhanry experimentingliverfibrosisdiagnosticbytwophotonexcitationmicroscopyandbagoffeaturesimageclassification |