Literature DB >> 29092947

An Image Analysis Resource for Cancer Research: PIIP-Pathology Image Informatics Platform for Visualization, Analysis, and Management.

Anne L Martel1,2, Dan Hosseinzadeh3, Caglar Senaras4, Yu Zhou5, Azadeh Yazdanpanah6, Rushin Shojaii6, Emily S Patterson4, Anant Madabhushi5, Metin N Gurcan4.   

Abstract

Pathology Image Informatics Platform (PIIP) is an NCI/NIH sponsored project intended for managing, annotating, sharing, and quantitatively analyzing digital pathology imaging data. It expands on an existing, freely available pathology image viewer, Sedeen. The goal of this project is to develop and embed some commonly used image analysis applications into the Sedeen viewer to create a freely available resource for the digital pathology and cancer research communities. Thus far, new plugins have been developed and incorporated into the platform for out of focus detection, region of interest transformation, and IHC slide analysis. Our biomarker quantification and nuclear segmentation algorithms, written in MATLAB, have also been integrated into the viewer. This article describes the viewing software and the mechanism to extend functionality by plugins, brief descriptions of which are provided as examples, to guide users who want to use this platform. PIIP project materials, including a video describing its usage and applications, and links for the Sedeen Viewer, plug-ins, and user manuals are freely available through the project web page: http://pathiip.org Cancer Res; 77(21); e83-86. ©2017 AACR. ©2017 American Association for Cancer Research.

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Year:  2017        PMID: 29092947      PMCID: PMC5679396          DOI: 10.1158/0008-5472.CAN-17-0323

Source DB:  PubMed          Journal:  Cancer Res        ISSN: 0008-5472            Impact factor:   12.701


  11 in total

1.  High-throughput biomarker segmentation on ovarian cancer tissue microarrays via hierarchical normalized cuts.

Authors:  Andrew Janowczyk; Sharat Chandran; Rajendra Singh; Dimitra Sasaroli; George Coukos; Michael D Feldman; Anant Madabhushi
Journal:  IEEE Trans Biomed Eng       Date:  2011-12-13       Impact factor: 4.538

2.  Increasing specimen coverage using digital whole-mount breast pathology: implementation, clinical feasibility and application in research.

Authors:  Gina M Clarke; Chris Peressotti; Paul Constantinou; Danoush Hosseinzadeh; Anne Martel; Martin J Yaffe
Journal:  Comput Med Imaging Graph       Date:  2011-06-11       Impact factor: 4.790

Review 3.  Histopathological image analysis: a review.

Authors:  Metin N Gurcan; Laura E Boucheron; Ali Can; Anant Madabhushi; Nasir M Rajpoot; B Yener
Journal:  IEEE Rev Biomed Eng       Date:  2009-10-30

4.  Developing the Quantitative Histopathology Image Ontology (QHIO): A case study using the hot spot detection problem.

Authors:  Metin N Gurcan; John Tomaszewski; James A Overton; Scott Doyle; Alan Ruttenberg; Barry Smith
Journal:  J Biomed Inform       Date:  2016-12-18       Impact factor: 6.317

Review 5.  Emerging Themes in Image Informatics and Molecular Analysis for Digital Pathology.

Authors:  Rohit Bhargava; Anant Madabhushi
Journal:  Annu Rev Biomed Eng       Date:  2016-07-11       Impact factor: 9.590

Review 6.  Image analysis and machine learning in digital pathology: Challenges and opportunities.

Authors:  Anant Madabhushi; George Lee
Journal:  Med Image Anal       Date:  2016-07-04       Impact factor: 8.545

7.  Supervised multi-view canonical correlation analysis (sMVCCA): integrating histologic and proteomic features for predicting recurrent prostate cancer.

Authors:  George Lee; Asha Singanamalli; Haibo Wang; Michael D Feldman; Stephen R Master; Natalie N C Shih; Elaine Spangler; Timothy Rebbeck; John E Tomaszewski; Anant Madabhushi
Journal:  IEEE Trans Med Imaging       Date:  2014-09-05       Impact factor: 10.048

8.  A quantitative histomorphometric classifier (QuHbIC) identifies aggressive versus indolent p16-positive oropharyngeal squamous cell carcinoma.

Authors:  James S Lewis; Sahirzeeshan Ali; Jingqin Luo; Wade L Thorstad; Anant Madabhushi
Journal:  Am J Surg Pathol       Date:  2014-01       Impact factor: 6.394

9.  Going fully digital: Perspective of a Dutch academic pathology lab.

Authors:  Nikolas Stathonikos; Mitko Veta; André Huisman; Paul J van Diest
Journal:  J Pathol Inform       Date:  2013-06-29

10.  Classification of follicular lymphoma: the effect of computer aid on pathologists grading.

Authors:  Mohammad Faizal Ahmad Fauzi; Michael Pennell; Berkman Sahiner; Weijie Chen; Arwa Shana'ah; Jessica Hemminger; Alejandro Gru; Habibe Kurt; Michael Losos; Amy Joehlin-Price; Christina Kavran; Stephen M Smith; Nicholas Nowacki; Sharmeen Mansor; Gerard Lozanski; Metin N Gurcan
Journal:  BMC Med Inform Decis Mak       Date:  2015-12-30       Impact factor: 2.796

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  22 in total

1.  The transition module: a method for preventing overfitting in convolutional neural networks.

Authors:  S Akbar; M Peikari; S Salama; S Nofech-Mozes; A L Martel
Journal:  Comput Methods Biomech Biomed Eng Imaging Vis       Date:  2018-01-26

2.  Biomedical Image Processing with Containers and Deep Learning: An Automated Analysis Pipeline: Data architecture, artificial intelligence, automated processing, containerization, and clusters orchestration ease the transition from data acquisition to insights in medium-to-large datasets.

Authors:  Germán González; Conor L Evans
Journal:  Bioessays       Date:  2019-05-16       Impact factor: 4.345

3.  Explainable Biomarkers for Automated Glomerular and Patient-Level Disease Classification.

Authors:  Matthew Nicholas Basso; Moumita Barua; Rohan John; April Khademi
Journal:  Kidney360       Date:  2021-12-09

4.  Quantitative digital histopathology and machine learning to predict pathological complete response to chemotherapy in breast cancer patients using pre-treatment tumor biopsies.

Authors:  Khadijeh Saednia; Andrew Lagree; Marie A Alera; Lauren Fleshner; Audrey Shiner; Ethan Law; Brianna Law; David W Dodington; Fang-I Lu; William T Tran; Ali Sadeghi-Naini
Journal:  Sci Rep       Date:  2022-06-11       Impact factor: 4.996

5.  Serum-derived carcinoembryonic antigen (CEA) activates fibroblasts to induce a local re-modeling of the extracellular matrix that favors the engraftment of CEA-expressing tumor cells.

Authors:  Aws Abdul-Wahid; Marzena Cydzik; Nicholas W Fischer; Aaron Prodeus; John E Shively; Anne Martel; Samira Alminawi; Zeina Ghorab; Neil L Berinstein; Jean Gariépy
Journal:  Int J Cancer       Date:  2018-08-09       Impact factor: 7.396

Review 6.  Harnessing non-destructive 3D pathology.

Authors:  Jonathan T C Liu; Adam K Glaser; Kaustav Bera; Lawrence D True; Nicholas P Reder; Kevin W Eliceiri; Anant Madabhushi
Journal:  Nat Biomed Eng       Date:  2021-02-15       Impact factor: 25.671

7.  Machine learning techniques for mitoses classification.

Authors:  Shima Nofallah; Sachin Mehta; Ezgi Mercan; Stevan Knezevich; Caitlin J May; Donald Weaver; Daniela Witten; Joann G Elmore; Linda Shapiro
Journal:  Comput Med Imaging Graph       Date:  2020-11-27       Impact factor: 4.790

8.  A Cluster-then-label Semi-supervised Learning Approach for Pathology Image Classification.

Authors:  Mohammad Peikari; Sherine Salama; Sharon Nofech-Mozes; Anne L Martel
Journal:  Sci Rep       Date:  2018-05-08       Impact factor: 4.379

Review 9.  Advanced Morphologic Analysis for Diagnosing Allograft Rejection: The Case of Cardiac Transplant Rejection.

Authors:  Eliot G Peyster; Anant Madabhushi; Kenneth B Margulies
Journal:  Transplantation       Date:  2018-08       Impact factor: 4.939

Review 10.  Twenty Years of Digital Pathology: An Overview of the Road Travelled, What is on the Horizon, and the Emergence of Vendor-Neutral Archives.

Authors:  Liron Pantanowitz; Ashish Sharma; Alexis B Carter; Tahsin Kurc; Alan Sussman; Joel Saltz
Journal:  J Pathol Inform       Date:  2018-11-21
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