Literature DB >> 22925782

State of the art and trends for digital pathology.

Marcial García Rojo1.   

Abstract

Anatomic pathology is a medical specialty where both information management systems and digital images systems paly a most important role. Digital pathology is a new concept that considers all uses of this information, including diagnosis, biomedical research and education. Virtual microscopy or whole slide imaging, resulting in digital slides, is an outreaching technology in anatomic pathology. Limiting factors in the expansion of virtual microscopy are formidable storage dimension, scanning speed, quality of image and cultural change. Anatomic pathology data and images should be an important part of the patient electronic health records as well as of clinical data warehouse, epidemiological or biomedical research databases, and platforms dedicated to translational medicine. Integrating anatomic pathology to the "healthcare enterprise" can only be achieved using existing and emerging medical informatics standards like Digital Imaging and Communications in Medicine (DICOM®<sup>1</sup>), Health Level Seven (HL7®), and Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT®), following the recommendations of Integrating the Healthcare Enterprise (IHE®). The consequences of the full digitalization of pathology departments are hard to foresee, but short term issues have arisen that imply interesting challenges for health care standards bodies.

Entities:  

Mesh:

Year:  2012        PMID: 22925782

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  4 in total

1.  Microscopic nuclei classification, segmentation, and detection with improved deep convolutional neural networks (DCNN).

Authors:  Zahangir Alom; Vijayan K Asari; Anil Parwani; Tarek M Taha
Journal:  Diagn Pathol       Date:  2022-04-19       Impact factor: 3.196

Review 2.  Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review.

Authors:  Fuyong Xing; Lin Yang
Journal:  IEEE Rev Biomed Eng       Date:  2016-01-06

3.  Quantification of spatial tumor heterogeneity in immunohistochemistry staining images.

Authors:  Inna Chervoneva; Amy R Peck; Misung Yi; Boris Freydin; Hallgeir Rui
Journal:  Bioinformatics       Date:  2021-06-16       Impact factor: 6.937

4.  iPathology cockpit diagnostic station: validation according to College of American Pathologists Pathology and Laboratory Quality Center recommendation at the Hospital Trust and University of Verona.

Authors:  Matteo Brunelli; Serena Beccari; Romano Colombari; Stefano Gobbo; Luca Giobelli; Andrea Pellegrini; Marco Chilosi; Maria Lunardi; Guido Martignoni; Aldo Scarpa; Albino Eccher
Journal:  Diagn Pathol       Date:  2014-12-19       Impact factor: 2.644

  4 in total

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