Digital pathology and computational image analysis in nephropathology

Barisoni, Laura; Lafata, Kyle; Hewitt, Stephen M.; Madabhushi, Anant; Balis, Ulysses

doi:10.1038/s41581-020-0321-6

Cited by 164 publications

(120 citation statements)

References 123 publications

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“…Quantitative digital image analysis methods of lung morphometry have been introduced, but a limitation is their requirement of manual interventions (21)(22)(23)(24)(25)(26)(27)(28). Consequently, these techniques are laborious, time consuming, and subject to researcher intra-and inter-variabilities.…”

Section: Discussionmentioning

confidence: 99%

“…Ashcroft scoring utilizes a numerical scale from 0 through 8 to grade fibrosis; it is subject to intra-and inter-scorer variability (20). Computerized digital image analysis was developed to utilize a continuous measure of fibrosis in various rodent or human tissues, including lung, liver, heart, and kidney (21)(22)(23)(24)(25)(26)(27)(28). Most computerized digital image analyses evaluate severity of fibrosis by quantifying collagen content stained by picrosirius red or to a lesser extent by Masson trichrome or immunostaining.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated Digital Quantification of Pulmonary Fibrosis in Human Histopathology Specimens

Testa

Julé²,

Lundh

et al. 2021

Front. Med.

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Pulmonary fibrosis is characterized by abnormal interstitial extracellular matrix and cellular accumulations. Methods quantifying fibrosis severity in lung histopathology samples are semi-quantitative, subjective, and analyze only portions of sections. We sought to determine whether automated computerized imaging analysis shown to continuously measure fibrosis in mice could also be applied in human samples. A pilot study was conducted to analyze a small number of specimens from patients with Hermansky-Pudlak syndrome pulmonary fibrosis (HPSPF) or idiopathic pulmonary fibrosis (IPF). Digital images of entire lung histological serial sections stained with picrosirius red and alcian blue or anti-CD68 antibody were analyzed using dedicated software to automatically quantify fibrosis, collagen, and macrophage content. Automated fibrosis quantification based on parenchymal tissue density and fibrosis score measurements was compared to pulmonary function values or Ashcroft score. Automated fibrosis quantification of HPSPF lung explants was significantly higher than that of IPF lung explants or biopsies and was also significantly higher in IPF lung explants than in IPF biopsies. A high correlation coefficient was found between some automated quantification measurements and lung function values for the three sample groups. Automated quantification of collagen content in lung sections used for digital image analyses was similar in the three groups. CD68 immunolabeled cell measurements were significantly higher in HPSPF explants than in IPF biopsies. In conclusion, computerized image analysis provides access to accurate, reader-independent pulmonary fibrosis quantification in human histopathology samples. Fibrosis, collagen content, and immunostained cells can be automatically and individually quantified from serial sections. Robust automated digital image analysis of human lung samples enhances the available tools to quantify and study fibrotic lung disease.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Digital Quantification of Pulmonary Fibrosis in Human Histopathology Specimens

Testa

Julé²,

Lundh

et al. 2021

Front. Med.

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“…Despite challenges described above, the stepand-repeat scanning method has achieved a remarkable success in whole-slide pathological imaging. [62][63][64][65] To address these issues, Weinstein et al 7 developed a parallelization scheme using an array of microscopes [Figs. 2(a) and 2(b)].…”

Section: Array Microscopymentioning

confidence: 99%

Review of bio-optical imaging systems with a high space-bandwidth product

Park

Brady

Zheng³

et al. 2021

Adv. Photon.

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Optical imaging has served as a primary method to collect information about biosystems across scales-from functionalities of tissues to morphological structures of cells and even at biomolecular levels. However, to adequately characterize a complex biosystem, an imaging system with a number of resolvable points, referred to as a space-bandwidth product (SBP), in excess of one billion is typically needed. Since a gigapixel-scale far exceeds the capacity of current optical imagers, compromises must be made to obtain either a low spatial resolution or a narrow field-of-view (FOV). The problem originates from constituent refractive optics-the larger the aperture, the more challenging the correction of lens aberrations. Therefore, it is impractical for a conventional optical imaging system to achieve an SBP over hundreds of millions. To address this unmet need, a variety of high-SBP imagers have emerged over the past decade, enabling an unprecedented resolution and FOV beyond the limit of conventional optics. We provide a comprehensive survey of high-SBP imaging techniques, exploring their underlying principles and applications in bioimaging.

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“…This and other key definitions are shown in Table 2. The definitions in Table 2 and throughout this article are based on our own experience, expert group publications, 1,8 and useful reviews [9][10][11][12][13][14][15][16] ; however, we recognise that variable definitions are provided and used in other publications, and are probably in flux.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples

Farris¹,

Vizcarra

Amgad

et al. 2021

Histopathology

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Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examplesWhole slide imaging, which is an important technique in the field of digital pathology, has recently been the subject of increased interest and avenues for utilisation, and with more widespread whole slide image (WSI) utilisation, there will also be increased interest in and implementation of image analysis (IA) techniques. IA includes artificial intelligence (AI) and targeted or hypothesis-driven algorithms. In the overall pathology field, the number of citations related to these topics has increased in recent years. Renal pathology is one anatomical pathology subspecialty that has utilised WSIs and IA algorithms; it can be argued that renal transplant pathology could be particularly suited for whole slide imaging and IA, as renal transplant pathology is frequently classified by use of the semiquantitative Banff classification of renal allograft pathology. Hypothesis-driven/targeted algorithms have been used in the past for the assessment of a variety of features in the kidney (e.g. interstitial fibrosis, tubular atrophy, inflammation); in recent years, the amount of research has particularly increased in the area of AI/machine learning for the identification of glomeruli, for histological segmentation, and for other applications. Deep learning is the form of machine learning that is most often used for such AI approaches to the 'big data' of pathology WSIs, and deep learning methods such as artificial neural networks (ANNs)/convolutional neural networks (CNNs) are utilised. Unsupervised and supervised AI algorithms can be employed to accomplish image or semantic classification. In this review, AI and other IA algorithms applied to WSIs are discussed, and examples from renal pathology are covered, with an emphasis on renal transplant pathology.

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Digital pathology and computational image analysis in nephropathology

Cited by 164 publications

References 123 publications

Automated Digital Quantification of Pulmonary Fibrosis in Human Histopathology Specimens

Automated Digital Quantification of Pulmonary Fibrosis in Human Histopathology Specimens

Review of bio-optical imaging systems with a high space-bandwidth product

Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples

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