Automatic segmentation of lungs in SPECT images using active shape model trained by meshes delineated in CT images
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Summary
A fully automated method for segmentation of 3D SPECT ventilation and perfusion images using the Active Shape Model to generate accurate anatomic results in SPECT images with functional information and thus unclear borders.
- Type
- article
- Published
- 2016-08-01
- Cited by
- 3
- References
- 11
- OpenAlex
- https://openalex.org/W2535946666
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:21471722
Keywords
Segmentation, Artificial intelligence, Sørensen–Dice coefficient, Computer science, Computer vision
References
- Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool
- A statistical model-based approach for the automatic quantitative analysis of perfusion gated SPECT studies
- Grading obstructive lung disease using tomographic pulmonary scintigraphy in patients with chronic obstructive pulmonary disease (COPD) and long-term smokers
- Impact of ventilation/perfusion single-photon emission computed tomography on treatment duration of pulmonary embolism
- Active shape model segmentation with optimal features
- Perfusion-Ventilation Lung SPECT Image Analysis System Based on Minimum Cross-Entropy Threshold and Watershed Segmentation
- SPECT imaging in the diagnosis of pulmonary embolism: automated detection of match and mismatch defects by means of image-processing techniques.
- Active appearance models: Theory and cases
- Statistical models of appearance for computer vision
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