ArticleAnnals of nuclear medicine2018
Automatic lung segmentation in functional SPECT images using active shape models trained on reference lung shapes from CT.
Article in Annals of nuclear medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 13 citations in OpenAlex.
- A deep learning method for translating 3DCT to SPECT ventilation imaging: First comparison withMedical physics · 2022Article
- Semi-supervised segmentation of metastasis lesions in bone scan images.Frontiers in molecular biosciences · 2022Article
- Automatic delineation and quantification of pulmonary vascular obstruction index in patients with pulmonary embolism using Perfusion SPECT-CT: a simulation study.EJNMMI physics · 2021Article
- 2D Statistical Lung Shape Analysis Using Chest Radiographs: Modelling and Segmentation.Journal of digital imaging · 2021Article
- [Research progress in lung parenchyma segmentation based on computed tomography].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021Review
- Deep learning based automatic segmentation of metastasis hotspots in thorax bone SPECT images.PloS one · 2020Article
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8 authors at 2 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
objectiveImage 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.
methodsA 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.
resultsThe 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).
conclusionAutomated 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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