Chih-Yang Hsiao1,2,3, Xiao Teng4, Tung-Hung Su1,5,6, Po-Huang Lee1,2, Jia-Horng Kao1,5,6, Kai-Wen Huang1,2,6. 1. Graduate Institute of Clinical Medicine, National Taiwan University College of Medicine, Taipei. 2. Department of Surgery, National Taiwan University Hospital, Taipei. 3. Department of Traumatology, National Taiwan University Hospital, Taipei. 4. HistoIndex Pte Ltd., Singapore. 5. Department of Internal Medicine, National Taiwan University Hospital, Taipei. 6. Hepatitis Research Center, National Taiwan University Hospital, Taipei.
Abstract
BACKGROUND: Second harmonic generation (SHG)/two-photon excited fluorescence (TPEF) microscopy is commonly used for the quantitative assessment of liver fibrosis; however, the accuracy is susceptible to sampling error and count error due to disturbances induced by some forms of collagen in liver specimens. In this study, we sought to improve the accuracy of quantitative assessments by removing the effects of this disturbing collagen and optimizing the sampling protocol. METHODS: Large liver resection samples from 111 patients with chronic hepatitis B were scanned using SHG/TPEF microscopy with multiple adjacent images. During the quantitative assessment, we then removed SHG signals associated with three types of extraneous physiological collagen: large patches of collagen near the boundary of the capsule, collagen around tubular structures, and collagen associated with distorted vessel walls. The optimal sampling protocol was identified by comparing scans from regions of interest of various sizes (3×3 tiles and 5×5 tiles) with full scans of the same tissue. RESULTS: The proposed auto-correction algorithm detected 88 of 97 (90.7%) disturbing collagen on the images from the validation set. Removing these signals of disturbing collagen improved the correlation between Metavir stage and quantification of all 41 proposed collagen features. Through optimal sampling, five scans of 5×5 tiles or ten scans of 3×3 tiles were sufficient to minimize the mean error rate to around 2% of collagen percentage quantification and to achieve similar correlations around 0.27 with Metavir stage as using full tissue scans. CONCLUSIONS: Our results demonstrate that the quantitative assessments of liver fibrosis can be greatly enhanced in terms of accuracy and efficiency through optimal sampling and the automated removal of disturbing collagen signals. These types of image processing could be integrated in next-generation SHG/TPEF microscopic systems. 2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.
BACKGROUND: Second harmonic generation (SHG)/two-photon excited fluorescence (TPEF) microscopy is commonly used for the quantitative assessment of liver fibrosis; however, the accuracy is susceptible to sampling error and count error due to disturbances induced by some forms of collagen in liver specimens. In this study, we sought to improve the accuracy of quantitative assessments by removing the effects of this disturbing collagen and optimizing the sampling protocol. METHODS: Large liver resection samples from 111 patients with chronic hepatitis B were scanned using SHG/TPEF microscopy with multiple adjacent images. During the quantitative assessment, we then removed SHG signals associated with three types of extraneous physiological collagen: large patches of collagen near the boundary of the capsule, collagen around tubular structures, and collagen associated with distorted vessel walls. The optimal sampling protocol was identified by comparing scans from regions of interest of various sizes (3×3 tiles and 5×5 tiles) with full scans of the same tissue. RESULTS: The proposed auto-correction algorithm detected 88 of 97 (90.7%) disturbing collagen on the images from the validation set. Removing these signals of disturbing collagen improved the correlation between Metavir stage and quantification of all 41 proposed collagen features. Through optimal sampling, five scans of 5×5 tiles or ten scans of 3×3 tiles were sufficient to minimize the mean error rate to around 2% of collagen percentage quantification and to achieve similar correlations around 0.27 with Metavir stage as using full tissue scans. CONCLUSIONS: Our results demonstrate that the quantitative assessments of liver fibrosis can be greatly enhanced in terms of accuracy and efficiency through optimal sampling and the automated removal of disturbing collagen signals. These types of image processing could be integrated in next-generation SHG/TPEF microscopic systems. 2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.
Authors: K Grønbaek; P B Christensen; S Hamilton-Dutoit; B H Federspiel; E Hage; O J Jensen; M Vyberg Journal: J Viral Hepat Date: 2002-11 Impact factor: 3.728
Authors: Shuoyu Xu; Yan Wang; Dean C S Tai; Shi Wang; Chee Leong Cheng; Qiwen Peng; Jie Yan; Yongpeng Chen; Jian Sun; Xieer Liang; Youfu Zhu; Jagath C Rajapakse; Roy E Welsch; Peter T C So; Aileen Wee; Jinlin Hou; Hanry Yu Journal: J Hepatol Date: 2014-02-26 Impact factor: 25.083
Authors: Robert Matthew Kottmann; Jesse Sharp; Kristina Owens; Peter Salzman; Guang-Qian Xiao; Richard P Phipps; Patricia J Sime; Edward B Brown; Seth W Perry Journal: Respir Res Date: 2015-05-27