Automatic segmentation of lungs in SPECT images using active shape model trained by meshes delineated in CT images
(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.
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- author
- Grigorios-Aris, Cheimariotis ; Al-Mashat, Mariam LU ; Kostas, Haris ; Anthony, Aletras H. ; Jögi, Jonas LU ; Bajc, Marika LU ; Nicolaos, Maglaveras and Heiberg, Einar LU
- organization
- publishing date
- 2016-10-13
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
- volume
- 2016-October
- article number
- 7590940
- pages
- 4 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
- conference location
- Orlando, United States
- conference dates
- 2016-08-16 - 2016-08-20
- external identifiers
-
- pmid:28268559
- 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
- 2024-03-07 21:21:30
@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 = {{IEEE - 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}}, doi = {{10.1109/EMBC.2016.7590940}}, volume = {{2016-October}}, year = {{2016}}, }