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Segmentation challenge on the quantification of left atrial wall thickness

Research output: Chapter in Book/Report/Conference proceedingConference paper

Rashed Karim, Marta Varela, Pranav Bhagirath, Ross Morgan, Jonathan Behar, James Housden, Ronak Rajani, Oleg Aslanidi, Kawal S. Rhode

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer‐Verlag Berlin Heidelberg
Pages193-200
Number of pages8
Volume10124 LNCS
ISBN (Print)9783319527178
DOIs
StatePublished - 1 Feb 2017
Event7th International Workshop on Statistical Atlases and Computational Models of the Heart Imaging and Modelling Challenges, STACOM 2016 Held in Conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece
Duration: 17 Oct 201621 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10124 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference7th International Workshop on Statistical Atlases and Computational Models of the Heart Imaging and Modelling Challenges, STACOM 2016 Held in Conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
CountryGreece
CityAthens
Period17/10/201621/10/2016

Documents

King's Authors

Abstract

This paper presents an image database for the Left Atrial Wall Thickness Quantification challenge at the MICCAI STACOM 2016 workshop along with some preliminary results. The image database consists of both CT (n = 10) and MRI (n = 10) datasets. Expert delineations from two observers were obtained for each image in the CT set and a single-observer segmentation was obtained for each image in the MRI set included in this study. Computer algorithms for segmentation of wall thickness from three research groups contributed to this challenge. The algorithms were evaluated on the basis of wall thickness measurements obtained from the segmentation masks.

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