QuPath: Open source software for digital pathology image analysis
Peter Bankhead,Maurice B Loughrey,Maurice B Loughrey,José A Fernández,Yvonne Dombrowski,Darragh G. McArt,Philip D Dunne,Stephen McQuaid,Stephen McQuaid,Ronan T. Gray,Liam J. Murray,Helen G. Coleman,Jacqueline A James,Jacqueline A James,Manuel Salto-Tellez,Manuel Salto-Tellez,Peter W. Hamilton +16 more
TL;DR: QuPath provides researchers with powerful batch-processing and scripting functionality, and an extensible platform with which to develop and share new algorithms to analyze complex tissue images, making it suitable for a wide range of additional image analysis applications across biomedical research.
read more
Abstract: QuPath is new bioimage analysis software designed to meet the growing need for a user-friendly, extensible, open-source solution for digital pathology and whole slide image analysis. In addition to offering a comprehensive panel of tumor identification and high-throughput biomarker evaluation tools, QuPath provides researchers with powerful batch-processing and scripting functionality, and an extensible platform with which to develop and share new algorithms to analyze complex tissue images. Furthermore, QuPath’s flexible design makes it suitable for a wide range of additional image analysis applications across biomedical research.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
The human tumor microbiome is composed of tumor type–specific intracellular bacteria
Deborah Nejman,Ilana Livyatan,Garold Fuks,Nancy Gavert,Yaara Zwang,Leore T. Geller,Aviva Rotter-Maskowitz,Roi Weiser,Roi Weiser,Giuseppe Mallel,Elinor Gigi,Arnon Meltser,Gavin M. Douglas,Iris Kamer,Vancheswaran Gopalakrishnan,Tali Dadosh,Smadar Levin-Zaidman,Sofia Avnet,Tehila Atlan,Zachary A. Cooper,Reetakshi Arora,Alexandria P. Cogdill,Abdul Wadud Khan,Gabriel Ologun,Yuval Bussi,Adina Weinberger,Maya Lotan-Pompan,Ofra Golani,Gili Perry,Merav Rokah,Keren Bahar-Shany,Elisa A. Rozeman,Christian U. Blank,Anat Ronai,Ron Shaoul,Amnon Amit,Amnon Amit,Tatiana Dorfman,Ran Kremer,Zvi R. Cohen,Zvi R. Cohen,Sagi Harnof,Sagi Harnof,Tali Siegal,Einav Yehuda-Shnaidman,Einav Nili Gal-Yam,Hagit Shapira,Nicola Baldini,Morgan G. I. Langille,Alon Ben-Nun,Alon Ben-Nun,Bella Kaufman,Bella Kaufman,Aviram Nissan,Talia Golan,Talia Golan,Maya Dadiani,Keren Levanon,Keren Levanon,Jair Bar,Jair Bar,Shlomit Yust-Katz,Shlomit Yust-Katz,Iris Barshack,Iris Barshack,Daniel S. Peeper,Dan J. Raz,Eran Segal,Jennifer A. Wargo,Judith Sandbank,Noam Shental,Ravid Straussman +71 more
TL;DR: A comprehensive analysis of the tumor microbiome was undertaken, studying 1526 tumors and their adjacent normal tissues across seven cancer types, finding that each tumor type has a distinct microbiome composition and that breast cancer has a particularly rich and diverse microbiome.
1.6K
Resolving the fibrotic niche of human liver cirrhosis at single cell level
Prakash Ramachandran,Ross Dobie,John R. Wilson-Kanamori,Elena Dora,Beth E. P. Henderson,N T Luu,N T Luu,Jordan R. Portman,Kylie P. Matchett,M Brice,John A. Marwick,Richard S Taylor,Mirjana Efremova,Roser Vento-Tormo,Neil O. Carragher,Timothy J. Kendall,Jonathan A. Fallowfield,Ewen M Harrison,David R. Mole,David R. Mole,Stephen J. Wigmore,Stephen J. Wigmore,Philip N. Newsome,Philip N. Newsome,Christopher J. Weston,Christopher J. Weston,John P. Iredale,Frank Tacke,Jeffrey W. Pollard,Jeffrey W. Pollard,Chris P. Ponting,John C. Marioni,John C. Marioni,John C. Marioni,Sarah A. Teichmann,Sarah A. Teichmann,Sarah A. Teichmann,Neil C. Henderson +37 more
TL;DR: Analysis of transcriptomes of more than 100,000 single human cells yields molecular definitions for non-parenchymal cell types that are found in healthy and cirrhotic human liver, and identifies markers for scar-associated macrophages and endothelial cells.
Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images
Simon Graham,Quoc Dang Vu,Shan E Ahmed Raza,Ayesha Azam,Yee Wah Tsang,Jin Tae Kwak,Nasir M. Rajpoot +6 more
TL;DR: A novel convolutional neural network is presented for simultaneous nuclear segmentation and classification that leverages the instance-rich information encoded within the vertical and horizontal distances of nuclear pixels to their centres of mass to separate clustered nuclei, resulting in an accurate segmentation.
1.1K
CellProfiler 4: improvements in speed, utility and usability.
David R. Stirling,Madison J. Swain-Bowden,Alice Lucas,Anne E. Carpenter,Beth A. Cimini,Allen Goodman +5 more
TL;DR: The CellProfiler 4 as discussed by the authors is a new version of this software with expanded functionality based on user feedback, and it has made several user interface refinements to improve the usability of the software.
Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study.
Jakob Nikolas Kather,Johannes Krisam,Pornpimol Charoentong,Pornpimol Charoentong,Tom Luedde,Esther Herpel,Cleo Aron Weis,Timo Gaiser,Alexander Marx,Nektarios A. Valous,Nektarios A. Valous,Dyke Ferber,Dyke Ferber,Lina Jansen,Constantino Carlos Reyes-Aldasoro,Inka Zörnig,Inka Zörnig,Dirk Jäger,Dirk Jäger,Hermann Brenner,Jenny Chang-Claude,Michael Hoffmeister,Niels Halama +22 more
TL;DR: It is shown that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images and was an independent prognostic factor for overall survival in a multivariable Cox proportional hazard model.
References
CellProfiler: free, versatile software for automated biological image analysis.
TL;DR: The use of the open-source software, CellProfiler, to automatically identify and measure a variety of biological objects in images is described, enabling biologists to comprehensively and quantitatively address many questions that previously would have required custom programming.
960
Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK): Explanation and Elaboration
TL;DR: The REMARK “elaboration and explanation” guideline, by Doug Altman and colleagues, provides a detailed reference for authors on important issues to consider when designing, conducting, and analyzing tumor marker prognostic studies.
An International Ki67 Reproducibility Study
Mei Yin C. Polley,Samuel C Y Leung,Lisa M. McShane,Dongxia Gao,Judith Hugh,Mauro G. Mastropasqua,Giuseppe Viale,Lila Zabaglo,Frédérique Penault-Llorca,John M. S. Bartlett,Allen M. Gown,W. Fraser Symmans,Tammy Piper,Erika Mehl,Rebecca A. Enos,Daniel F. Hayes,Mitch Dowsett,Torsten O. Nielsen +17 more
TL;DR: Substantial variability in Ki67 scoring was observed among some of the world's most experienced laboratories, and factors contributing to interlaboratory discordance included tumor region selection, counting method, and subjective assessment of staining positivity.
Reporting recommendations for tumor marker prognostic studies (REMARK): explanation and elaboration
TL;DR: The REMARK checklist is expanded to enhance its use and effectiveness through better understanding of the intent of each item and why the information is important to report.
Reporting and Methods in Clinical Prediction Research: A Systematic Review
Walter Bouwmeester,Nicolaas P.A. Zuithoff,Susan Mallett,Mirjam I. Geerlings,Yvonne Vergouwe,Yvonne Vergouwe,Ewout W. Steyerberg,Douglas G. Altman test,Karel G.M. Moons +8 more
TL;DR: It is found that the majority of prediction studies do not follow current methodological recommendations and need to be re-examined.