Contact us
|
Home
|
Login
| Users Online: 725
Feedback
Subscribe
Advertise
Search
Advanced Search
Month wise articles
Figures next to the month indicate the number of articles in that month
2021
January
[
1
]
2020
November
[
3
]
August
[
1
]
July
[
1
]
May
[
1
]
February
[
1
]
2019
December
[
2
]
September
[
1
]
August
[
2
]
July
[
2
]
June
[
1
]
May
[
1
]
April
[
1
]
March
[
1
]
February
[
2
]
2018
December
[
4
]
November
[
1
]
August
[
1
]
July
[
1
]
May
[
1
]
2017
October
[
1
]
September
[
3
]
June
[
1
]
May
[
1
]
March
[
1
]
February
[
1
]
2016
April
[
1
]
March
[
1
]
January
[
2
]
2015
October
[
3
]
September
[
3
]
June
[
4
]
March
[
2
]
January
[
1
]
2014
October
[
2
]
September
[
2
]
August
[
2
]
July
[
1
]
June
[
1
]
May
[
1
]
March
[
1
]
January
[
2
]
2013
December
[
2
]
November
[
1
]
July
[
1
]
June
[
1
]
March
[
2
]
2012
December
[
1
]
September
[
3
]
August
[
1
]
July
[
1
]
April
[
3
]
March
[
1
]
February
[
1
]
2011
August
[
2
]
July
[
2
]
June
[
1
]
May
[
1
]
March
[
2
]
January
[
1
]
2010
October
[
3
]
» Articles published in the past year
To view other articles click corresponding year from the navigation links on the left side.
All
|
Abstracts
|
Book Review
|
Commentary
|
Editorial
|
Letters to Editor
|
Original Article
|
Original Articles
|
PV16 Abstracts
|
Research Article
|
Review Articles
|
Symposium
|
Technical Note
Export selected to
Endnote
Reference Manager
Procite
Medlars Format
RefWorks Format
BibTex Format
Show all abstracts
Show selected abstracts
Export selected to
Add to my list
Research Article:
Pathological diagnosis of gastric cancers with a novel computerized analysis system
Kosuke Oikawa, Akira Saito, Tomoharu Kiyuna, Hans Peter Graf, Eric Cosatto, Masahiko Kuroda
J Pathol Inform
2017, 8:5 (28 February 2017)
DOI
:10.4103/2153-3539.201114
PMID
:28400994
Background:
Recent studies of molecular biology have provided great advances for diagnostic molecular pathology. Automated diagnostic systems with computerized scanning for sampled cells in fluids or smears are now widely utilized. Automated analysis of tissue sections is, however, very difficult because they exhibit a complex mixture of overlapping malignant tumor cells, benign host-derived cells, and extracellular materials. Thus, traditional histological diagnosis is still the most powerful method for diagnosis of diseases.
Methods:
We have developed a novel computer-assisted pathology system for rapid, automated histological analysis of hematoxylin and eosin (H and E)-stained sections. It is a multistage recognition system patterned after methods that human pathologists use for diagnosis but harnessing machine learning and image analysis. The system first analyzes an entire H and E-stained section (tissue) at low resolution to search suspicious areas for cancer and then the selected areas are analyzed at high resolution to confirm the initial suspicion.
Results:
After training the pathology system with gastric tissues samples, we examined its performance using other 1905 gastric tissues. The system's accuracy in detecting malignancies was shown to be almost equal to that of conventional diagnosis by expert pathologists.
Conclusions:
Our novel computerized analysis system provides a support for histological diagnosis, which is useful for screening and quality control. We consider that it could be extended to be applicable to many other carcinomas after learning normal and malignant forms of various tissues. Furthermore, we expect it to contribute to the development of more objective grading systems, immunohistochemical staining systems, and fluorescent-stained image analysis systems.
[ABSTRACT]
[HTML Full text]
[PDF]
[Mobile Full text]
[EPub]
[Citations (1) ]
[PubMed]
[Sword Plugin for Repository]
Beta
Sitemap
|
What's New
|
Feedback
|
Disclaimer
|
© Journal of Pathology Informatics | Published by Wolters Kluwer -
Medknow
Online since 10
th
March, 2010