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

Cheimariotis Grigorios-Aris; Mariam Al-Mashat; Haris Kostas; Aletras H. Anthony; Jonas Jögi; Marika Bajc; Maglaveras Nicolaos; Einar Heiberg

doi:10.1109/EMBC.2016.7590940

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

Cheimariotis Grigorios-Aris, Mariam Al-Mashat, Haris Kostas, Aletras H. Anthony, Jonas Jögi, Marika Bajc, Maglaveras Nicolaos, Einar Heiberg

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

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 precision.

Original language	English
Title of host publication	2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Publisher	IEEE - Institute of Electrical and Electronics Engineers Inc.
Pages	1280-1283
Number of pages	4
Volume	2016-October
ISBN (Electronic)	9781457702204
DOIs	https://doi.org/10.1109/EMBC.2016.7590940
Publication status	Published - 2016 Oct 13
Event	38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States Duration: 2016 Aug 16 → 2016 Aug 20

Conference

Conference	38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Country/Territory	United States
City	Orlando
Period	2016/08/16 → 2016/08/20

Subject classification (UKÄ)

Radiology, Nuclear Medicine and Medical Imaging

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10.1109/EMBC.2016.7590940

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Grigorios-Aris, C., Al-Mashat, M., Kostas, H., Anthony, A. H., Jögi, J., Bajc, M., Nicolaos, M., & Heiberg, E. (2016). Automatic segmentation of lungs in SPECT images using active shape model trained by meshes delineated in CT images. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 (Vol. 2016-October, pp. 1280-1283). Article 7590940 IEEE - Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2016.7590940

Grigorios-Aris, Cheimariotis ; Al-Mashat, Mariam ; Kostas, Haris et al. / 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. Vol. 2016-October IEEE - Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1280-1283

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

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 precision.",

author = "Cheimariotis Grigorios-Aris and Mariam Al-Mashat and Haris Kostas and Anthony, {Aletras H.} and Jonas J{\"o}gi and Marika Bajc and Maglaveras Nicolaos and Einar Heiberg",

year = "2016",

month = oct,

day = "13",

doi = "10.1109/EMBC.2016.7590940",

language = "English",

volume = "2016-October",

pages = "1280--1283",

booktitle = "2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016",

publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 ; Conference date: 16-08-2016 Through 20-08-2016",

}

Grigorios-Aris, C, Al-Mashat, M, Kostas, H, Anthony, AH, Jögi, J , Bajc, M, Nicolaos, M & Heiberg, E 2016, Automatic segmentation of lungs in SPECT images using active shape model trained by meshes delineated in CT images. in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. vol. 2016-October, 7590940, IEEE - Institute of Electrical and Electronics Engineers Inc., pp. 1280-1283, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016, Orlando, United States, 2016/08/16. https://doi.org/10.1109/EMBC.2016.7590940

Automatic segmentation of lungs in SPECT images using active shape model trained by meshes delineated in CT images. / Grigorios-Aris, Cheimariotis; Al-Mashat, Mariam; Kostas, Haris et al.
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Vol. 2016-October IEEE - Institute of Electrical and Electronics Engineers Inc., 2016. p. 1280-1283 7590940.

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

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AU - Grigorios-Aris, Cheimariotis

AU - Al-Mashat, Mariam

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AU - Anthony, Aletras H.

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AU - Nicolaos, Maglaveras

AU - Heiberg, Einar

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N2 - 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.

AB - 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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Grigorios-Aris C, Al-Mashat M, Kostas H, Anthony AH, Jögi J , Bajc M et al. Automatic segmentation of lungs in SPECT images using active shape model trained by meshes delineated in CT images. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Vol. 2016-October. IEEE - Institute of Electrical and Electronics Engineers Inc. 2016. p. 1280-1283. 7590940 doi: 10.1109/EMBC.2016.7590940

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

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