| 1. |
- Cheimariotis, Grigorios Aris, et al.
(författare)
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Automatic lung segmentation in functional SPECT images using active shape models trained on reference lung shapes from CT
- 2018
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Ingår i: Annals of Nuclear Medicine. - : Springer Science and Business Media LLC. - 0914-7187 .- 1864-6433. ; 32:2, s. 94-104
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Tidskriftsartikel (refereegranskat)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 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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| 2. |
- Grigorios-Aris, Cheimariotis, et al.
(författare)
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Automatic segmentation of lungs in SPECT images using active shape model trained by meshes delineated in CT images
- 2016
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Ingår i: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. - 9781457702204 ; 2016-October, s. 1280-1283
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Konferensbidrag (refereegranskat)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.
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