Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Rashed Karim, Marta Varela, Pranav Bhagirath, Ross Morgan, Jonathan Behar, James Housden, Ronak Rajani, Oleg Aslanidi, Kawal S. Rhode
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Publisher | Springer‐Verlag Berlin Heidelberg |
| Pages | 193-200 |
| Number of pages | 8 |
| Volume | 10124 LNCS |
| ISBN (Print) | 9783319527178 |
| DOIs | |
| Accepted/In press | 19 Jul 2016 |
| E-pub ahead of print | 24 Jan 2017 |
| Published | 1 Feb 2017 |
| Additional links | |
| Event | 7th 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 2016 → 21 Oct 2016 |
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 10124 LNCS |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
| Conference | 7th 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 |
|---|---|
| Country/Territory | Greece |
| City | Athens |
| Period | 17/10/2016 → 21/10/2016 |
Segmentation challenge on the quantification_KARIM_Accepted_GREEN AAM
resultspaper.pdf, 835 KB, application/pdf
Uploaded date:20 Feb 2017
Version:Accepted author manuscript
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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