Wei Keat Tey1, Ye Chow Kuang2, Melanie Po-Leen Ooi3, Joon Joon Khoo4. 1. Advanced Engineering Platform and Department of Electrical and Computer Systems Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway 47500, Malaysia. Electronic address: tey.wei.keat@monash.edu. 2. Advanced Engineering Platform and Department of Electrical and Computer Systems Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway 47500, Malaysia. 3. Unitec Institute of Technology, 139 Carrington Road, Mount Albert, Auckland 1025, New Zealand. 4. Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway 47500, Malaysia.
Abstract
Interstitial fibrosis in renal biopsy samples is a scarring tissue structure that may be visually quantified by pathologists as an indicator to the presence and extent of chronic kidney disease. The standard method of quantification by visual evaluation presents reproducibility issues in the diagnoses. This study proposes an automated quantification system for measuring the amount of interstitial fibrosis in renal biopsy images as a consistent basis of comparison among pathologists. The system extracts and segments the renal tissue structures based on colour information and structural assumptions of the tissue structures. The regions in the biopsy representing the interstitial fibrosis are deduced through the elimination of non-interstitial fibrosis structures from the biopsy area and quantified as a percentage of the total area of the biopsy sample. A ground truth image dataset has been manually prepared by consulting an experienced pathologist for the validation of the segmentation algorithms. The results from experiments involving experienced pathologists have demonstrated a good correlation in quantification result between the automated system and the pathologists' visual evaluation. Experiments investigating the variability in pathologists also proved the automated quantification error rate to be on par with the average intra-observer variability in pathologists' quantification. BACKGROUND AND OBJECTIVE: Interstitial fibrosis in renal biopsy samples is a scarring tissue structure that may be visually quantified by pathologists as an indicator to the presence and extent of chronic kidney disease. The standard method of quantification by visual evaluation presents reproducibility issues in the diagnoses due to the uncertainties in human judgement. METHODS: An automated quantification system for accurately measuring the amount of interstitial fibrosis in renal biopsy images is presented as a consistent basis of comparison among pathologists. The system identifies the renal tissue structures through knowledge-based rules employing colour space transformations and structural features extraction from the images. In particular, the renal glomerulus identification is based on a multiscale textural feature analysis and a support vector machine. The regions in the biopsy representing interstitial fibrosis are deduced through the elimination of non-interstitial fibrosis structures from the biopsy area. The experiments conducted evaluate the system in terms of quantification accuracy, intra- and inter-observer variability in visual quantification by pathologists, and the effect introduced by the automated quantification system on the pathologists' diagnosis. RESULTS: A 40-image ground truth dataset has been manually prepared by consulting an experienced pathologist for the validation of the segmentation algorithms. The results from experiments involving experienced pathologists have demonstrated an average error of 9 percentage points in quantification result between the automated system and the pathologists' visual evaluation. Experiments investigating the variability in pathologists involving samples from 70 kidney patients also proved the automated quantification error rate to be on par with the average intra-observer variability in pathologists' quantification. CONCLUSIONS: The accuracy of the proposed quantification system has been validated with the ground truth dataset and compared against the pathologists' quantification results. It has been shown that the correlation between different pathologists' estimation of interstitial fibrosis area has significantly improved, demonstrating the effectiveness of the quantification system as a diagnostic aide.
Interstitial fibrosis in renal biopsy samples is a scarring tissue structure that may be visually quantified by pathologists as an indicator to the presence and extent of chronic kidney disease. The standard method of quantification by visual evaluation presents reproducibility issues in the diagnoses. This study proposes an automated quantification system for measuring the amount of interstitial fibrosis in renal biopsy images as a consistent basis of comparison among pathologists. The system extracts and segments the renal tissue structures based on colour information and structural assumptions of the tissue structures. The regions in the biopsy representing the interstitial fibrosis are deduced through the elimination of non-interstitial fibrosis structures from the biopsy area and quantified as a percentage of the total area of the biopsy sample. A ground truth image dataset has been manually prepared by consulting an experienced pathologist for the validation of the segmentation algorithms. The results from experiments involving experienced pathologists have demonstrated a good correlation in quantification result between the automated system and the pathologists' visual evaluation. Experiments investigating the variability in pathologists also proved the automated quantification error rate to be on par with the average intra-observer variability in pathologists' quantification. BACKGROUND AND OBJECTIVE: Interstitial fibrosis in renal biopsy samples is a scarring tissue structure that may be visually quantified by pathologists as an indicator to the presence and extent of chronic kidney disease. The standard method of quantification by visual evaluation presents reproducibility issues in the diagnoses due to the uncertainties in human judgement. METHODS: An automated quantification system for accurately measuring the amount of interstitial fibrosis in renal biopsy images is presented as a consistent basis of comparison among pathologists. The system identifies the renal tissue structures through knowledge-based rules employing colour space transformations and structural features extraction from the images. In particular, the renal glomerulus identification is based on a multiscale textural feature analysis and a support vector machine. The regions in the biopsy representing interstitial fibrosis are deduced through the elimination of non-interstitial fibrosis structures from the biopsy area. The experiments conducted evaluate the system in terms of quantification accuracy, intra- and inter-observer variability in visual quantification by pathologists, and the effect introduced by the automated quantification system on the pathologists' diagnosis. RESULTS: A 40-image ground truth dataset has been manually prepared by consulting an experienced pathologist for the validation of the segmentation algorithms. The results from experiments involving experienced pathologists have demonstrated an average error of 9 percentage points in quantification result between the automated system and the pathologists' visual evaluation. Experiments investigating the variability in pathologists involving samples from 70 kidneypatients also proved the automated quantification error rate to be on par with the average intra-observer variability in pathologists' quantification. CONCLUSIONS: The accuracy of the proposed quantification system has been validated with the ground truth dataset and compared against the pathologists' quantification results. It has been shown that the correlation between different pathologists' estimation of interstitial fibrosis area has significantly improved, demonstrating the effectiveness of the quantification system as a diagnostic aide.
Authors: Luisa Ricaurte Archila; Aleksandar Denic; Aidan F Mullan; Ramya Narasimhan; Marija Bogojevic; R Houston Thompson; Bradley C Leibovich; S Jeson Sangaralingham; Maxwell L Smith; Mariam P Alexander; Andrew D Rule Journal: J Am Soc Nephrol Date: 2021-06-18 Impact factor: 14.978