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Automatic lung segmentation in functional SPECT images using active shape models trained on reference lung shapes from CT

Cheimariotis, Grigorios Aris; Al-Mashat, Mariam LU ; Haris, Kostas; Aletras, Anthony H. LU ; Jögi, Jonas LU ; Bajc, Marika LU ; Maglaveras, Nicolaos and Heiberg, Einar LU (2017) In Annals of Nuclear Medicine p.1-11
Abstract

Objective: Image segmentation is an essential step in quantifying the extent of reduced or absent lung function. The aim of this study is to develop and validate a new tool for automatic segmentation of lungs in ventilation and perfusion SPECT images and compare automatic and manual SPECT lung segmentations with reference computed tomography (CT) volumes. Methods: A total of 77 subjects (69 patients with obstructive lung disease, and 8 subjects without apparent perfusion of ventilation loss) performed low-dose CT followed by ventilation/perfusion (V/P) SPECT examination in a hybrid gamma camera system. In the training phase, lung shapes from the 57 anatomical low-dose CT images were used to construct two active shape models (right lung... (More)

Objective: Image segmentation is an essential step in quantifying the extent of reduced or absent lung function. The aim of this study is to develop and validate a new tool for automatic segmentation of lungs in ventilation and perfusion SPECT images and compare automatic and manual SPECT lung segmentations with reference computed tomography (CT) volumes. Methods: A total of 77 subjects (69 patients with obstructive lung disease, and 8 subjects without apparent perfusion of ventilation loss) performed low-dose CT followed by ventilation/perfusion (V/P) SPECT examination in a hybrid gamma camera system. In the training phase, lung shapes from the 57 anatomical low-dose CT images were used to construct two active shape models (right lung and left lung) which were then used for image segmentation. The algorithm was validated in 20 patients, comparing its results to reference delineation of corresponding CT images, and by comparing automatic segmentation to manual delineations in SPECT images. Results: The Dice coefficient between automatic SPECT delineations and manual SPECT delineations were 0.83 ± 0.04% for the right and 0.82 ± 0.05% for the left lung. There was statistically significant difference between reference volumes from CT and automatic delineations for the right (R = 0.53, p = 0.02) and left lung (R = 0.69, p < 0.001) in SPECT. There were similar observations when comparing reference volumes from CT and manual delineations in SPECT images, left lung (bias was − 10 ± 491, R = 0.60, p = 0.005) right lung (bias 36 ± 524 ml, R = 0.62, p = 0.004). Conclusion: Automated segmentation on SPECT images are on par with manual segmentation on SPECT images. Relative large volumetric differences between manual delineations of functional SPECT images and anatomical CT images confirms that lung segmentation of functional SPECT images is a challenging task. The current algorithm is a first step towards automatic quantification of wide range of measurements.

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author
organization
publishing date
type
Contribution to journal
publication status
epub
subject
keywords
Active shape model, CT, Image segmentation, V/P SPECT
in
Annals of Nuclear Medicine
pages
11 pages
publisher
Springer
external identifiers
  • scopus:85037979193
ISSN
0914-7187
DOI
10.1007/s12149-017-1223-y
language
English
LU publication?
yes
id
8d9276a3-6b61-44d6-9ff5-a75d37619eeb
date added to LUP
2018-01-04 11:43:05
date last changed
2018-01-10 11:55:07
@article{8d9276a3-6b61-44d6-9ff5-a75d37619eeb,
  abstract     = {<p>Objective: Image segmentation is an essential step in quantifying the extent of reduced or absent lung function. The aim of this study is to develop and validate a new tool for automatic segmentation of lungs in ventilation and perfusion SPECT images and compare automatic and manual SPECT lung segmentations with reference computed tomography (CT) volumes. Methods: A total of 77 subjects (69 patients with obstructive lung disease, and 8 subjects without apparent perfusion of ventilation loss) performed low-dose CT followed by ventilation/perfusion (V/P) SPECT examination in a hybrid gamma camera system. In the training phase, lung shapes from the 57 anatomical low-dose CT images were used to construct two active shape models (right lung and left lung) which were then used for image segmentation. The algorithm was validated in 20 patients, comparing its results to reference delineation of corresponding CT images, and by comparing automatic segmentation to manual delineations in SPECT images. Results: The Dice coefficient between automatic SPECT delineations and manual SPECT delineations were 0.83 ± 0.04% for the right and 0.82 ± 0.05% for the left lung. There was statistically significant difference between reference volumes from CT and automatic delineations for the right (R = 0.53, p = 0.02) and left lung (R = 0.69, p &lt; 0.001) in SPECT. There were similar observations when comparing reference volumes from CT and manual delineations in SPECT images, left lung (bias was − 10 ± 491, R = 0.60, p = 0.005) right lung (bias 36 ± 524 ml, R = 0.62, p = 0.004). Conclusion: Automated segmentation on SPECT images are on par with manual segmentation on SPECT images. Relative large volumetric differences between manual delineations of functional SPECT images and anatomical CT images confirms that lung segmentation of functional SPECT images is a challenging task. The current algorithm is a first step towards automatic quantification of wide range of measurements.</p>},
  author       = {Cheimariotis, Grigorios Aris and Al-Mashat, Mariam and Haris, Kostas and Aletras, Anthony H. and Jögi, Jonas and Bajc, Marika and Maglaveras, Nicolaos and Heiberg, Einar},
  issn         = {0914-7187},
  keyword      = {Active shape model,CT,Image segmentation,V/P SPECT},
  language     = {eng},
  month        = {12},
  pages        = {1--11},
  publisher    = {Springer},
  series       = {Annals of Nuclear Medicine},
  title        = {Automatic lung segmentation in functional SPECT images using active shape models trained on reference lung shapes from CT},
  url          = {http://dx.doi.org/10.1007/s12149-017-1223-y},
  year         = {2017},
}