Robust automated polyp detection for low-dose and normal-dose virtual colonoscopy

J.F. Peters, S.E. Grigorescu, R. Truyen, F.A. Gerritsen, A.H. de Vries, R.E. van Gelder, P. Rogalla

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)

Abstract

We evaluated the performance of an automated polyp-detection scheme on both low-dose and normal-dose virtual colonoscopy data from different institutions. The polyp-detection algorithm is statistically trained on low-dose virtual colonoscopy data from 13 patients with a predicted sensitivity of 92% and a false-positive rate of 9 objects per study. An independent test on data from 50 patients with similar preparation and acquisition as the training data and 32 patients having completely different preparation and acquisition confirms the prediction. Our results show that it is possible to design a sensitive and specific polyp-detector that is robust for low-dose and normal-dose, isotropic and anisotropic, for faecal-tagged and non-tagged CT colonoscopy data.
Original languageEnglish
Title of host publicationCARS 2005 : computer assisted radiology and surgery ; proceedings of the 19th international congress and exhibition, Berlin, Germany, June 22 - 25, 2005
EditorsH.U. Lemke
Place of PublicationAmsterdam
PublisherElsevier
Pages1146-1150
ISBN (Print)0-444-51872-X
DOIs
Publication statusPublished - 2005
EventCARS 2005 : International Congress and Exhibition Computer Assisted Radiology and Surgery - Berlin, Germany
Duration: 22 Jun 200525 Jun 2005

Publication series

NameInternational Congress Series
Volume1281
ISSN (Print)0531-5131

Conference

ConferenceCARS 2005 : International Congress and Exhibition Computer Assisted Radiology and Surgery
Abbreviated titleCARS 2005
Country/TerritoryGermany
CityBerlin
Period22/06/0525/06/05
OtherCARS 2005 : International Congress and Exhibition Computer Assisted Radiology and Surgery ; 19 (Berlin) : 2005.06.22-25

Fingerprint

Dive into the research topics of 'Robust automated polyp detection for low-dose and normal-dose virtual colonoscopy'. Together they form a unique fingerprint.

Cite this