15th International Symposium on Medical Information Processing and Analysis 2020
DOI: 10.1117/12.2542580
|View full text |Cite
|
Sign up to set email alerts
|

Automatic segmentation of the left ventricle myocardium by a multi-view deformable model

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

0
3
0

Year Published

2021
2021
2022
2022

Publication Types

Select...
2
1

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(3 citation statements)
references
References 0 publications
0
3
0
Order By: Relevance
“…The approach is divided into two categories: LV localization and contour segmentation, including shape feature detection [33], [34], LV segmentation and function estimation using deep learning [35]- [39]; automatic identification of blood vessel using a cascading classifier [40], diffusion-based unsupervised clustering technique for Myocardial motion patterns classification [41]; CNN and U-Net approach [42], Multi-input fusion network [43], cardiac motion measurement by used algorithm namely surface structure feature matching [44], deep earning method used deformable, level set and threshold method for automatic LV contour segmentation [45]- [48]. Another major challenge is a region of interest (ROI) for automatic contour segmentation.…”
Section: Fully Automatic Approachmentioning
confidence: 99%
“…The approach is divided into two categories: LV localization and contour segmentation, including shape feature detection [33], [34], LV segmentation and function estimation using deep learning [35]- [39]; automatic identification of blood vessel using a cascading classifier [40], diffusion-based unsupervised clustering technique for Myocardial motion patterns classification [41]; CNN and U-Net approach [42], Multi-input fusion network [43], cardiac motion measurement by used algorithm namely surface structure feature matching [44], deep earning method used deformable, level set and threshold method for automatic LV contour segmentation [45]- [48]. Another major challenge is a region of interest (ROI) for automatic contour segmentation.…”
Section: Fully Automatic Approachmentioning
confidence: 99%
“…The approach is divided into two categories: LV localization and contour segmentation, including shape feature detection [33], [34], LV segmentation and function estimation using deep learning [35]- [39]; automatic identification of blood vessel using a cascading classifier [40], diffusion-based unsupervised clustering technique for Myocardial motion patterns classification [41]; CNN and U-Net approach [42], Multi-input fusion network [43], cardiac motion measurement by used algorithm namely surface structure feature matching [44], deep earning method used deformable, level set and threshold method for automatic LV contour segmentation [45]- [48]. Another major challenge is a region of interest (ROI) for automatic contour segmentation.…”
Section: Fully Automatic Approachmentioning
confidence: 99%
“…Traditional image based methodologies including thresholding, region growing, region restricted, active contours (Dakua & Sahambi, 2011a, 2011bDe Alexandria et al, 2014), graph-cut (Dakua, 2014), and such other methods provide good accuracy, but show incapability to determine the ventricular surfaces in all cardiac phases (Ciyamala Kushbu & Inbamalar, 2021). Numerous absolutely distinct principles have also been used previously including deformable models (Beltran, Atehortúa, & Romero, 2020;Cordero-Grande et al, 2011) which improve the robustness and certainty of left ventricle, random forests to automatically select the discriminative features for LV segmentation. Segmentation was even considered as tracking the cardiac dynamics.…”
Section: Related Workmentioning
confidence: 99%