scholarly journals A User-Friendly Tool For Cloud-Based Whole Slide Image Segmentation, With Examples From Renal Histopathology

2021 ◽  
Author(s):  
Brendon Lutnick ◽  
David Manthey ◽  
Jan Becker ◽  
Brandon Ginley ◽  
Katharina Moos ◽  
...  
Keyword(s):  
Best Practices ◽  
Interstitial Fibrosis ◽  
Histological Structure ◽  
Murine Models ◽  
Tubular Atrophy ◽  
Model Research ◽  
Slide Image ◽  
Vascular Structures ◽  
User Friendly ◽  
Whole Slide Images

Abstract We have developed Histo-Cloud, a tool for segmentation of whole slide images (WSIs) that has an easy-to-use graphical user interface. This tool runs a state-of-the-art convolutional neural network (CNN) for segmentation of WSIs in the cloud and allows the extraction of features from segmented regions for further analysis. By segmenting glomeruli, interstitial fibrosis and tubular atrophy, and vascular structures from renal and non-renal WSIs, we demonstrate the scalability, best practices for transfer learning, and effects of dataset variability. Finally, we demonstrate an application for animal model research, analyzing glomerular features in murine models of aging, diabetic nephropathy, and HIV associated nephropathy. The ability to access this tool over the internet will facilitate widespread use by computational non-experts. Histo-Cloud is open source and adaptable for segmentation of any histological structure regardless of stain.

scholarly journals A user-friendly tool for cloud-based whole slide image segmentation, with examples from renal histopathology

2021 ◽  
Author(s):  
Brendon R Lutnick ◽  
David Manthey ◽  
Jan U Becker ◽  
Brandon Ginley ◽  
Katharina Moos ◽  
...  
Keyword(s):  
Machine Learning ◽  
User Interface ◽  
Interstitial Fibrosis ◽  
End Users ◽  
Great Promise ◽  
Histological Structure ◽  
Full Potential ◽  
Learning Tools ◽  
Tubular Atrophy ◽  
Slide Image

Image-based machine learning tools hold great promise for clinical applications in nephropathology and kidney research. However, the ideal end-users of these computational tools (e.g., pathologists and biological scientists) often face prohibitive challenges in using these tools to their full potential, including the lack of technical expertise, suboptimal user interface, and limited computation power. We have developed Histo-Cloud, a tool for segmentation of whole slide images (WSIs) that has an easy-to-use graphical user interface. This tool runs a state-of-the-art convolutional neural network (CNN) for segmentation of WSIs in the cloud and allows the extraction of features from segmented regions for further analysis. By segmenting glomeruli, interstitial fibrosis and tubular atrophy, and vascular structures from renal and non-renal WSIs, we demonstrate the scalability, best practices for transfer learning, and effects of dataset variability. Finally, we demonstrate an application for animal model research, analyzing glomerular features in murine models of aging, diabetic nephropathy, and HIV associated nephropathy. The ability to access this tool over the internet will facilitate widespread use by computational non-experts. Histo-Cloud is open source and adaptable for segmentation of any histological structure regardless of stain. Histo-Cloud will greatly accelerate and facilitate the generation of datasets for machine learning in the analysis of kidney histology, empowering computationally novice end-users to conduct deep feature analysis of tissue slides.

scholarly journals Automated Computational Detection of Interstitial Fibrosis, Tubular Atrophy, and Glomerulosclerosis

2021 ◽  
Vol 32 (4) ◽  
pp. 837-850 ◽  
Author(s):  
Brandon Ginley ◽  
Kuang-Yu Jen ◽  
Seung Seok Han ◽  
Luís Rodrigues ◽  
Sanjay Jain ◽  
...  
Keyword(s):  
Patient Outcome ◽  
Interstitial Fibrosis ◽  
Kidney Injury ◽  
Automated Identification ◽  
Statistical Tools ◽  
Tubular Atrophy ◽  
Biopsy Specimens ◽  
Chronic Kidney Injury ◽  
Modern Machine ◽  
Whole Slide Images

BackgroundInterstitial fibrosis, tubular atrophy (IFTA), and glomerulosclerosis are indicators of irrecoverable kidney injury. Modern machine learning (ML) tools have enabled robust, automated identification of image structures that can be comparable with analysis by human experts. ML algorithms were developed and tested for the ability to replicate the detection and quantification of IFTA and glomerulosclerosis that renal pathologists perform.MethodsA renal pathologist annotated renal biopsy specimens from 116 whole-slide images (WSIs) for IFTA and glomerulosclerosis. A total of 79 WSIs were used for training different configurations of a convolutional neural network (CNN), and 17 and 20 WSIs were used as internal and external testing cases, respectively. The best model was compared against the input of four renal pathologists on 20 new testing slides. Further, for 87 testing biopsy specimens, IFTA and glomerulosclerosis measurements made by pathologists and the CNN were correlated to patient outcome using classic statistical tools.ResultsThe best average performance across all image classes came from a DeepLab version 2 network trained at 40× magnification. IFTA and glomerulosclerosis percentages derived from this CNN achieved high levels of agreement with four renal pathologists. The pathologist- and CNN-based analyses of IFTA and glomerulosclerosis showed statistically significant and equivalent correlation with all patient-outcome variables.ConclusionsML algorithms can be trained to replicate the IFTA and glomerulosclerosis assessment performed by renal pathologists. This suggests computational methods may be able to provide a standardized approach to evaluate the extent of chronic kidney injury in situations in which renal-pathologist time is restricted or unavailable.

scholarly journals An Open-Source Whole Slide Image Registration Workflow at Cellular Precision Using Fiji, QuPath and Elastix

2022 ◽  
Vol 3 ◽  
Author(s):  
Nicolas Chiaruttini ◽  
Olivier Burri ◽  
Peter Haub ◽  
Romain Guiet ◽  
Jessica Sordet-Dessimoz ◽  
...  
Keyword(s):  
Multimodal Imaging ◽  
Cellular Level ◽  
Reference Image ◽  
Software Components ◽  
Cellular Resolution ◽  
Slide Image ◽  
Resolution Level ◽  
User Friendly ◽  
Automated Registration ◽  
Whole Slide Images

Image analysis workflows for Histology increasingly require the correlation and combination of measurements across several whole slide images. Indeed, for multiplexing, as well as multimodal imaging, it is indispensable that the same sample is imaged multiple times, either through various systems for multimodal imaging, or using the same system but throughout rounds of sample manipulation (e.g. multiple staining sessions). In both cases slight deformations from one image to another are unavoidable, leading to an imperfect superimposition Redundant and thus a loss of accuracy making it difficult to link measurements, in particular at the cellular level. Using pre-existing software components and developing missing ones, we propose a user-friendly workflow which facilitates the nonlinear registration of whole slide images in order to reach sub-cellular resolution level. The set of whole slide images to register and analyze is at first defined as a QuPath project. Fiji is then used to open the QuPath project and perform the registrations. Each registration is automated by using an elastix backend, or semi-automated by using BigWarp in order to interactively correct the results of the automated registration. These transformations can then be retrieved in QuPath to transfer any regions of interest from an image to the corresponding registered images. In addition, the transformations can be applied in QuPath to produce on-the-fly transformed images that can be displayed on top of the reference image. Thus, relevant data can be combined and analyzed throughout all registered slides, facilitating the analysis of correlative results for multiplexed and multimodal imaging.

scholarly journals TIAToolbox: An End-to-End Toolbox for Advanced Tissue Image Analytics

2021 ◽  
Author(s):  
Johnathan Pocock ◽  
Simon Graham ◽  
Quoc Dang Vu ◽  
Mostafa Jahanifar ◽  
Srijay Deshpande ◽  
...  
Keyword(s):  
Best Practices ◽  
Open Source Software ◽  
State Of The Art ◽  
Seamless Integration ◽  
Sheer Size ◽  
End To End ◽  
Computational Pathology ◽  
User Friendly ◽  
Pathology Image ◽  
Whole Slide Images

Computational Pathology (CPath) has seen rapid growth in recent years, driven by advanced deep learning (DL) algorithms. These algorithms typically share the same sequence of steps. However, due to the sheer size and complexity of handling large multi-gigapixel whole-slide images, there is no open-source software library that provides a generic end-to-end API for pathology image analysis using best practices for CPath. Most researchers have designed custom pipelines from the bottom-up, restricting the development of advanced CPath algorithms to specialist users. To help overcome this bottleneck, we present TIAToolbox, a Python toolbox designed to make CPath more accessible to new and advanced CPath scientists and pathologists alike. We provide a usable and adaptable library with efficient, cutting-edge and unit-tested tools for data loading, pre-processing, model inference, post-processing and visualization. This enables all kinds of users to easily build upon recent DL developments in the CPath literature. TIAToolbox provides a user-friendly modular API to enable seamless integration of advanced DL algorithms. We show with the help of examples how state-of-the-art DL algorithms can be streamlined using TIAToolbox.

scholarly journals A tool for federated training of segmentation models on whole slide images.

2021 ◽  
Author(s):  
Brendon R Lutnick ◽  
David Manthey ◽  
Jan U Becker ◽  
Jonathan E Zuckerman ◽  
Luis Rodrigues ◽  
...  
Keyword(s):  
Interstitial Fibrosis ◽  
Image Data ◽  
Medical Data ◽  
Training Data ◽  
Further Training ◽  
Tubular Atrophy ◽  
Data Heterogeneity ◽  
Computational Pathology ◽  
Segmentation Models ◽  
Whole Slide Images

The largest bottleneck to the development of convolutional neural network (CNN) models in the computational pathology domain is the collection and curation of diverse training datasets. Training CNNs requires large cohorts of image data, and model generalizability is dependent on training data heterogeneity. Including data from multiple centers enhances the generalizability of CNN based models, but this is hindered by the logistical challenges of sharing medical data. In this paper we explore the feasibility of training our recently developed cloud-based segmentation tool (Histo-Cloud) using federated learning. We show that a federated trained model to segment interstitial fibrosis and tubular atrophy (IFTA) using datasets from three institutions is comparable to a model trained by pooling the data on one server when tested on a fourth (holdout) institution's data. Further, training a model to segment glomeruli for a federated dataset (split by staining) demonstrates similar performance.

scholarly journals Using Participatory Design to Design an eHealth Well-Being Program

2021 ◽  
Vol 18 (14) ◽  
pp. 7250
Author(s):  
Yannick van Hierden ◽  
Timo Dietrich ◽  
Sharyn Rundle-Thiele
Keyword(s):  
Best Practices ◽  
Health Professionals ◽  
Participatory Design ◽  
Online Community ◽  
Well Being ◽  
Proof Of Concept ◽  
End User ◽  
Ehealth Intervention ◽  
Pilot Intervention ◽  
User Friendly

In recent years, the relevance of eHealth interventions has become increasingly evident. However, a sequential procedural application to cocreating eHealth interventions is currently lacking. This paper demonstrates the implementation of a participatory design (PD) process to inform the design of an eHealth intervention aiming to enhance well-being. PD sessions were conducted with 57 people across four sessions. Within PD sessions participants experienced prototype activities, provided feedback and designed program interventions. A 5-week eHealth well-being intervention focusing on lifestyle, habits, physical activity, and meditation was proposed. The program is suggested to be delivered through online workshops and online community interaction. A five-step PD process emerged; namely, (1) collecting best practices, (2) participatory discovery, (3) initial proof-of-concept, (4) participatory prototyping, and (5) pilot intervention proof-of-concept finalisation. Health professionals, behaviour change practitioners and program planners can adopt this process to ensure end-user cocreation using the five-step process. The five-step PD process may help to create user-friendly programs.

Identifying patients with CKD risk at the time of nephrectomy: When to initiate nephrology consult

2021 ◽  
pp. 239936932110319
Author(s):  
Yihe Yang ◽  
Zachary Kozel ◽  
Purva Sharma ◽  
Oksana Yaskiv ◽  
Jose Torres ◽  
...  
Keyword(s):  
Risk Factors ◽  
Multivariate Analysis ◽  
Radical Nephrectomy ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Interstitial Fibrosis ◽  
Kidney Neoplasm ◽  
Tubular Atrophy ◽  
Independent Risk Factors

Introduction: The prevalence of chronic kidney disease (CKD) is high among kidney neoplasm patients because of the overlapping risk factors. Our purpose is to identify kidney cancer survivors with higher CKD risk. Methods: We studied a retrospective cohort of 361 kidney tumor patients with partial or radical nephrectomy. Linear mixed model was performed. Results: Of patients with follow-up >3 months, 84% were identified retrospectively to fulfill criteria for CKD diagnosis, although CKD was documented in only 15%. Urinalysis was performed in 205 (57%) patients at the time of nephrectomy. Multivariate analysis showed interstitial fibrosis and tubular atrophy (IFTA) >25% ( p = 0.005), severe arteriolar sclerosis ( p = 0.013), female gender ( p = 0.024), older age ( p = 0.012), BMI ⩾ 25 kg/m2 ( p < 0.001), documented CKD ( p < 0.001), baseline eGFR ⩽ 60 ml/min/1.73 m2 ( p < 0.001), and radical nephrectomy ( p < 0.001) were independent risk factors of lower eGFR at baseline and during follow-up. Average eGFR decreased within 3 months post nephrectomy. However, patients with different risk levels showed different eGFR time trend pattern at longer follow-ups. Multivariate analysis of time × risk factor interaction showed BMI, radical nephrectomy and baseline eGFR had time-dependent impact. BMI ⩾ 25 kg/m2 and radical nephrectomy were associated with steeper eGFR decrease slope. In baseline eGFR > 90 ml/min/1.73 m2 group, eGFR rebounded to pre-nephrectomy levels during extended follow-up. In partial nephrectomy patients with baseline eGFR ⩾ 90 ml/min/1.73 m2 ( n = 61), proteinuria ( p < 0.001) and BMI ( p < 0.001) were independent risk factors of decreased eGFR during follow up. Conclusions: As have been suggested by others and confirmed by our study, proteinuria and CKD are greatly under-recognized. Although self-evident as a minimum workup for nephrectomy patients to include SCr, eGFR, urinalysis, and proteinuria, the need for uniform applications of this practice should be reinforced. Non-neoplastic histology evaluation is valuable and should include an estimate of global sclerosis% (GS) and IFTA%. Patients with any proteinuria and/or eGFR ⩽ 60 at the time of nephrectomy or in follow-up with urologists, and/or >25% GS or IFTA, should be referred for early nephrology consultation.

scholarly journals Cellular Mechanisms of Tissue Fibrosis. 3. Novel mechanisms of kidney fibrosis

2013 ◽  
Vol 304 (7) ◽  
pp. C591-C603 ◽  
Author(s):  
Gabriela Campanholle ◽  
Giovanni Ligresti ◽  
Sina A. Gharib ◽  
Jeremy S. Duffield
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Interstitial Fibrosis ◽  
Kidney Injury ◽  
Innate Immune ◽  
Cellular Mechanisms ◽  
Tubular Atrophy ◽  
Kidney Fibrosis ◽  
Capillary Rarefaction ◽  
Peritubular Capillaries

Chronic kidney disease, defined as loss of kidney function for more than three months, is characterized pathologically by glomerulosclerosis, interstitial fibrosis, tubular atrophy, peritubular capillary rarefaction, and inflammation. Recent studies have identified a previously poorly appreciated, yet extensive population of mesenchymal cells, called either pericytes when attached to peritubular capillaries or resident fibroblasts when embedded in matrix, as the progenitors of scar-forming cells known as myofibroblasts. In response to sustained kidney injury, pericytes detach from the vasculature and differentiate into myofibroblasts, a process not only causing fibrosis, but also directly contributing to capillary rarefaction and inflammation. The interrelationship of these three detrimental processes makes myofibroblasts and their pericyte progenitors an attractive target in chronic kidney disease. In this review, we describe current understanding of the mechanisms of pericyte-to-myofibroblast differentiation during chronic kidney disease, draw parallels with disease processes in the glomerulus, and highlight promising new therapeutic strategies that target pericytes or myofibroblasts. In addition, we describe the critical paracrine roles of epithelial, endothelial, and innate immune cells in the fibrogenic process.

Early Urinary CCL2 is Associated With the Later Development of Interstitial Fibrosis and Tubular Atrophy in Renal Allografts

2010 ◽  
Vol 90 (4) ◽  
pp. 394-400 ◽  
Author(s):  
Julie Ho ◽  
David N. Rush ◽  
Ian W. Gibson ◽  
Martin Karpinski ◽  
Leroy Storsley ◽  
...  
Keyword(s):  
Interstitial Fibrosis ◽  
Tubular Atrophy ◽  
Renal Allografts
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