ArticleInternational journal of computer assisted radiology and surgery2016
An SPCNN-GVF-based approach for the automatic segmentation of left ventricle in cardiac cine MR images.
Article in International journal of computer assisted radiology and surgery, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Cardiac Disease Classification Using Two-Dimensional Thickness and Few-Shot Learning Based on Magnetic Resonance Imaging Image Segmentation.Journal of imaging · 2022Article
- Image Segmentation-Based Cervical Spine MRI Images to Evaluate the Treatment of Patients with Chronic Pain.Computational and mathematical methods in medicine · 2022Article
- Adoption of Snake Variable Model-Based Method in Segmentation and Quantitative Calculation of Cardiac Ultrasound Medical Images.Journal of healthcare engineering · 2021Article
- Artificial intelligence in detection and segmentation of internal auditory canal and its nerves using deep learning techniques.International journal of computer assisted radiology and surgery · 2020Article
- Artificial intelligence in cardiac radiology.La Radiologia medica · 2020Review
- An integrated multi-objective whale optimized support vector machine and local texture feature model for severity prediction in subjects with cardiovascular disorder.International journal of computer assisted radiology and surgery · 2020Article
- Development and clinical validation of a hybrid method for semiautomated left ventricle endocardial and epicardial boundary extraction on cine-magnetic resonance images.Journal of medical imaging (Bellingham, Wash.) · 2018Article
- An automatic segmentation method of a parameter-adaptive PCNN for medical images.International journal of computer assisted radiology and surgery · 2017Article
- Automatic gallbladder and gallstone regions segmentation in ultrasound image.International journal of computer assisted radiology and surgery · 2017Article
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6 authors.
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Abstract
purposeAccurate segmentation of left ventricle (LV) is essential for the cardiac function analysis. However, it is labor intensive and time consuming for radiologists to delineate LV boundary manually. In this paper, we present a novel self-correcting framework for the fully automatic LV segmentation.
methodsFirstly, a time-domain method is designed to extract a rectangular region of interest around the heart. Then, the simplified pulse-coupled neural network (SPCNN) is employed to locate the LV cavity. Different from the existing approaches, SPCNN can realize the self-correcting segmentation due to its parameter controllability. Subsequently, the post-processing based on the maximum gradient searching is proposed to obtain the accurate endocardium. Finally, a new external force based on the shape similarity is defined and integrated into the gradient vector flow (GVF) snake with the balloon force to segment the epicardium.
resultsWe obtain encouraging segmentation results tested on the database provided by MICCAI 2009. The average percentage of good contours is 92.26 %, the average perpendicular distance is 2.38 mm, and the overlapping dice metric is 0.89. Besides, the experiment results show good correlations between the automatic segmentation and the manual delineation (for the LV ejection fraction and the LV myocardial mass, the correlation coefficients R are 0.9683 and 0.9278, respectively).
conclusionWe propose an effective and fast method combing the SPCNN and the improved GVF for the automatic segmentation of LV.
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