FIBER-ML, an Open-Source Supervised Machine Learning Tool for Quantification of Fibrosis in Tissue Sections - Archive ouverte HAL Access content directly
Journal Articles American Journal of Pathology Year : 2022

FIBER-ML, an Open-Source Supervised Machine Learning Tool for Quantification of Fibrosis in Tissue Sections

(1) , (1) , (1) , (1) , (1) , (1) , (1) , (1) , (1, 2) , (1, 2)
1
2

Abstract

Pathologic fibrosis is a major hallmark of tissue insult in many chronic diseases. Although the amount of fibrosis is recognized as a direct indicator of the extent of disease, there is no consentaneous method for its quantification in tissue sections. This study tested FIBER-ML, a semi-automated, open-source freeware that uses a machine-learning approach to quantify fibrosis automatically after a short user-controlled learning phase. Fibrosis was quantified in sirius red-stained tissue sections from two fibrogenic animal models: acute stress-induced cardiomyopathy in rats (Takotsubo syndrome-like) and HIV-induced nephropathy in mice (chronic kidney disease). The quantitative results of FIBER-ML software version 1.0 were compared with those of ImageJ in Takotsubo syndrome, and with those of inForm in chronic kidney disease. Intra- and inter-operator and inter-software correlation and agreement were assessed. All correlations were excellent (>0.95) in both data sets. The values of discriminatory power between the pathologic and healthy groups were <10-3 for data on Takotsubo syndrome and <10-4 for data on chronic kidney disease. Intra-operator agreement, assessed by intra-class coefficient correlation, was good (>0.8), while inter-operator and inter-software agreement ranged from moderate to good (>0.7). FIBER-ML performed in a fast and user-friendly manner, with reproducible and consistent quantification of fibrosis in tissue sections. It offers an open-source alternative to currently used software, including quality control and file management.
Loading...
Not file

Dates and versions

inserm-03788337 , version 1 (26-09-2022)

Identifiers

Cite

Caterina Facchin, Anais Certain, Thulaciga Yoganathan, Clement Delacroix, Alicia Arevalo Garcia, et al.. FIBER-ML, an Open-Source Supervised Machine Learning Tool for Quantification of Fibrosis in Tissue Sections. American Journal of Pathology, 2022, 192 (5), pp.783-793. ⟨10.1016/j.ajpath.2022.01.013⟩. ⟨inserm-03788337⟩

Collections

INSERM UP-SANTE
3 View
0 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More