Advanced Engineering Platform and Department of Electrical and Computer Systems Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway 47500, Malaysia.
Advanced Engineering Platform and Department of Electrical and Computer Systems Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway 47500, Malaysia.
Comput Methods Programs Biomed. 2018 Mar;155:109-120. doi: 10.1016/j.cmpb.2017.12.004. Epub 2017 Dec 12.
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.
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.
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.
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.
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.
肾活检样本中的间质纤维化是一种瘢痕组织结构，病理学家可以通过视觉评估将其量化，作为慢性肾脏病存在和严重程度的指标。由于人类判断的不确定性，视觉评估的标准量化方法在诊断中存在可重复性问题。
提出了一种自动量化系统，用于准确测量肾活检图像中间质纤维化的程度，为病理学家之间的比较提供一致的基础。该系统通过使用颜色空间变换和从图像中提取结构特征的基于知识的规则来识别肾组织结构。特别是，肾肾小球的识别是基于多尺度纹理特征分析和支持向量机。通过从活检区域中消除非间质纤维化结构来推断活检中代表间质纤维化的区域。实验评估了系统在量化准确性、病理学家视觉量化的内部和观察者间变异性以及自动化量化系统对病理学家诊断的影响方面的表现。
通过咨询一位经验丰富的病理学家，手动准备了一个包含 40 张图像的真实数据集，用于验证分割算法。涉及经验丰富的病理学家的实验结果表明，自动化系统和病理学家视觉评估之间的量化结果平均存在 9 个百分点的误差。涉及 70 名肾病患者样本的病理学家变异性实验也证明，自动化量化的误差率与病理学家量化的平均内部观察者变异性相当。
通过与真实数据集的验证和与病理学家的量化结果进行比较，已经验证了所提出的量化系统的准确性。结果表明，不同病理学家对间质纤维化区域估计的相关性显著提高，证明了该量化系统作为诊断辅助工具的有效性。
