PyHIST: A Histological Image Segmentation Tool

Muñoz-Aguirre, Manuel; Ntasis, Vasilis F.; Guigó, Roderic

doi:10.1101/2020.05.07.082461

Cited by 4 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, OpenHI2 - open source histopathological image platform (7) announced implementing VGG, GoogLeNet, MobileNet, and Inception architectures within their system for suggestive WSI segmentation. Furthermore, the move to AI-enrich annotation tools was facilitated by the introduction of PyHIST (8); an open source WSI tissue segmentation and preprocessing command-line tool for image patch (tile) generation for machine learning applications.…”

Section: Introductionmentioning

confidence: 99%

A deep learning-based iterative digital pathology annotation tool

Jaber¹,

Song

Beziaeva³

et al. 2021

Preprint

View full text Add to dashboard Cite

Well-annotated exemplars are an important prerequisite for supervised deep learning schemes. Unfortunately, generating these annotations is a cumbersome and laborious process, due to the large amount of time and effort needed. Here we present a deep-learning-based iterative digital pathology annotation tool that is both easy to use by pathologists and easy to integrate into machine vision systems. Our pathology image annotation tool greatly reduces annotation time from hours to a few minutes, while maintaining high fidelity with human-expert manual annotations. Here we demonstrate that our active learning tool can be used for a variety of pathology annotation tasks including masking tumor, stroma, and lymphocyte-rich regions, among others. This annotation automation system was validated on 90 unseen digital pathology images with tumor content from the CAMELYON16 database and it was found that pathologists' gold standard masks were re-produced successfully using our tool. That is, an average of 2.7 positive selections (mouse clicks) and 8.0 negative selections (mouse clicks) were sufficient to generate tumor masks similar to pathologists' gold standard in CAMELYON16 test WSIs. Furthermore, the developed image annotation tool has been used to build gold standard masks for hundreds of TCGA digital pathology images. This set was used to train a convolutional neural network for identification of tumor epithelium. The developed pan-cancer deep neural network was then tested on TCGA and internal data with comparable performance. The validated pathology image annotation tool described herein has the potential to be of great value in facilitating accurate, rapid pathological analysis of tumor biopsies.

show abstract

Section: Introductionmentioning

confidence: 99%

A deep learning-based iterative digital pathology annotation tool

Jaber¹,

Song

Beziaeva³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Efficient computation requires pre-segment tissue areas to avoid segmenting large areas of empty background space 54,55 ; we tackle this problem with traditional image processing techniques. In addition, tissue areas are typically too large at full resolution to process in random-access or GPU memory, and need to be tiled and the results stitched together.…”

Section: Introductionmentioning

confidence: 99%

“…Instead, we propose a DCNN based on QualityNet1 (Huang, Wu, and Meng 2016) that corrects each segmented object to account for cell overlap. Efficient computation requires pre-segment tissue areas to avoid segmenting large areas of empty background space (Kleczek et al, 2020;Muñoz-Aguirre et al, 2020); we tackle this problem with traditional image processing techniques. In addition, tissue areas are typically too large at full resolution to process in random-access or GPU memory, and need to be tiled and the results stitched together.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, tissue areas are typically too large at full resolution to process in random-access or GPU memory, and need to be tiled and the results stitched together. Uniform titling is commonly used (Coudray et al, 2018;Glastonbury et al, 2020;Muñoz-Aguirre et al, 2020) as it is simple to implement, but the tile overlap needed to avoid dropping cells on the tile seams produces redundant computations; in this work, we propose an adaptive tiling algorithm to reduce that burden. Furthermore, it is necessary to differentiate between the cells of interest (mature white adipocytes) and other WAT components and image artefacts.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Phenotyping of Klf14 mouse white adipose tissue enabled by whole slide segmentation with deep neural networks

Casero

Westerberg

Horner

et al. 2021

Preprint

View full text Add to dashboard Cite

White adipose tissue (WAT) plays a central role in metabolism, and multiple diseases and genetic mutations cause its remodeling, most notably obesity, which has reached pandemic levels. WAT is present in subcutaneous (SAT) and visceral (VAT) depots, and its main components are white adipocytes. Quantitative analysis of white adipocyte size and counts is of great interest to understand physiology and disease, due to intra- and inter-depot heterogeneity, as well as better prognosis for hypertrophy than hyperplasia, and for SAT expansion than VAT expansion. H&E histology of whole depot cross-sections provides excellent approximation of cell morphology and preserves spatial information. Previous studies have been limited to window subsampling of whole slides, and cell size analysis. In this paper, we present a deep learning pipeline that can segment overlapping white adipocytes on whole slides and filter out other cells. We also propose a statistical framework based on linear models to study WAT phenotypes with three interconnected levels (body weight BW, depot weight DW and quartile cell area). Applying it to find Klf14 phenotypes in mice using 147 whole slides of WAT H&E histology, we show sexual dimorphism, and different effects between depots, heterozygous parent of origin for the KO allele and genotype (WT vs. Het). In particular, whether variables are correlated (DW vs. BW and cell area vs. DW), and statistical differences between fitted linear models. We also find significant differences between hand-traced or window subsampling datasets and whole slide analysis. Finally, we provide heatmaps of cell size for all the slides, showing substantial spatial heterogeneity and local spatial correlations.

show abstract

Digital Pathology: A Comprehensive Review of Open-Source Histological Segmentation Software

Pavone,

Giannone,

Cabibi

et al. 2024

BioMedInformatics

View full text Add to dashboard Cite

In the era of digitalization, the biomedical sector has been affected by the spread of artificial intelligence. In recent years, the possibility of using deep and machine learning methods for clinical diagnostic and therapeutic interventions has been emerging as an essential resource for biomedical imaging. Digital pathology represents innovation in a clinical world that looks for faster and better-performing diagnostic methods, without losing the accuracy of current human-guided analyses. Indeed, artificial intelligence has played a key role in a wide variety of applications that require the analysis of a massive amount of data, including segmentation processes in medical imaging. In this context, artificial intelligence enables the improvement of image segmentation methods, moving towards the development of fully automated systems of analysis able to support pathologists in decision-making procedures. The aim of this review is to aid biologists and clinicians in discovering the most common segmentation open-source tools, including ImageJ (v. 1.54), CellProfiler (v. 4.2.5), Ilastik (v. 1.3.3) and QuPath (v. 0.4.3), along with their customized implementations. Additionally, the tools’ role in the histological imaging field is explored further, suggesting potential application workflows. In conclusion, this review encompasses an examination of the most commonly segmented tissues and their analysis through open-source deep and machine learning tools.

show abstract

PyHIST: A Histological Image Segmentation Tool

Abstract: 1

Cited by 4 publications

References 15 publications

A deep learning-based iterative digital pathology annotation tool

A deep learning-based iterative digital pathology annotation tool

Phenotyping of Klf14 mouse white adipose tissue enabled by whole slide segmentation with deep neural networks

Digital Pathology: A Comprehensive Review of Open-Source Histological Segmentation Software

Contact Info

Product

Resources

About