Advanced

Automatic segmentation of lungs in SPECT images using active shape model trained by meshes delineated in CT images

Grigorios-Aris, Cheimariotis; Al-Mashat, Mariam LU ; Kostas, Haris; Anthony, Aletras H.; Jögi, Jonas LU ; Bajc, Marika LU ; Nicolaos, Maglaveras and Heiberg, Einar LU (2016) 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 2016-October. p.1280-1283
Abstract

This paper presents a fully automated method for segmentation of 3D SPECT ventilation and perfusion images. It relies on statistical information on lung shape derived by CT manual segmentation and its main processing steps are: shape model extraction, binary segmentation, positioning of mean shape in SPECT images and iterative shape adaptation based on intensity profiles and on what is considered 'plausible' lung shape. The Active Shape Model is used to generate accurate anatomic results in SPECT images with functional information and thus unclear borders, especially in the case of pathologies. The method was compared against ground truth manual segmentation on CT images, using volumetric, difference dice coefficient, sensitivity and... (More)

This paper presents a fully automated method for segmentation of 3D SPECT ventilation and perfusion images. It relies on statistical information on lung shape derived by CT manual segmentation and its main processing steps are: shape model extraction, binary segmentation, positioning of mean shape in SPECT images and iterative shape adaptation based on intensity profiles and on what is considered 'plausible' lung shape. The Active Shape Model is used to generate accurate anatomic results in SPECT images with functional information and thus unclear borders, especially in the case of pathologies. The method was compared against ground truth manual segmentation on CT images, using volumetric, difference dice coefficient, sensitivity and precision.

(Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
volume
2016-October
pages
4 pages
publisher
Institute of Electrical and Electronics Engineers Inc.
conference name
38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
external identifiers
  • scopus:85009119435
ISBN
9781457702204
DOI
10.1109/EMBC.2016.7590940
language
English
LU publication?
yes
id
e9a3bce2-b774-4b47-8f1e-18a3b290acc6
date added to LUP
2017-02-01 08:52:13
date last changed
2017-05-04 03:00:16
@inproceedings{e9a3bce2-b774-4b47-8f1e-18a3b290acc6,
  abstract     = {<p>This paper presents a fully automated method for segmentation of 3D SPECT ventilation and perfusion images. It relies on statistical information on lung shape derived by CT manual segmentation and its main processing steps are: shape model extraction, binary segmentation, positioning of mean shape in SPECT images and iterative shape adaptation based on intensity profiles and on what is considered 'plausible' lung shape. The Active Shape Model is used to generate accurate anatomic results in SPECT images with functional information and thus unclear borders, especially in the case of pathologies. The method was compared against ground truth manual segmentation on CT images, using volumetric, difference dice coefficient, sensitivity and precision.</p>},
  author       = {Grigorios-Aris, Cheimariotis and Al-Mashat, Mariam and Kostas, Haris and Anthony, Aletras H. and Jögi, Jonas and Bajc, Marika and Nicolaos, Maglaveras and Heiberg, Einar},
  booktitle    = {2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016},
  isbn         = {9781457702204},
  language     = {eng},
  month        = {10},
  pages        = {1280--1283},
  publisher    = {Institute of Electrical and Electronics Engineers Inc.},
  title        = {Automatic segmentation of lungs in SPECT images using active shape model trained by meshes delineated in CT images},
  url          = {http://dx.doi.org/10.1109/EMBC.2016.7590940},
  volume       = {2016-October},
  year         = {2016},
}