Lecture Notes in Computer Science

2020

DOI: 10.1007/978-3-030-60334-2_6

|View full text |Cite

|

Sign up to set email alerts

|

Guidewire Segmentation in 4D Ultrasound Sequences Using Recurrent Fully Convolutional Networks

¹

,

²

,

³

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Results5

Citation Types

Supporting

0

Mentioning

7

Contrasting

0

Year Published

2023

2023

2023

2023

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(9 citation statements)

References 28 publications

Supporting

0

Mentioning

7

Contrasting

0

Order By: Relevance

“…Following the review of the title and abstract and, upon necessity, the full text further 187 records were rejected. After the reviewing process, a total of 36 original papers [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 ] and 20 conference proceedings [ 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 ,…”

Section: Resultsmentioning

confidence: 99%

“…Among the 56 selected papers, 16 (28.6%) proposed the application of DL to US images in cardiovascular studies. In particular, these papers focused on the heart [ 43 , 45 , 48 , 54 , 64 , 73 , 83 ], atherosclerotic plaque [ 34 ], carotid [ 56 ]/abdominal [ 40 ]/femoral [ 31 , 52 , 75 ] arteries, femoral veins [ 65 , 66 ] and inferior vena cava [ 77 ].…”

Section: Resultsmentioning

confidence: 99%

“…Nine papers [ 31 , 48 , 52 , 54 , 65 , 66 , 73 , 75 , 77 ] dealt with porcine models. In all these studies, the number of enrolled animals was very low (1, 10, 11, 7, 1, 1, 1, 5 and 2 pigs in [ 31 , 48 , 52 , 54 , 65 , 66 , 73 , 75 , 77 ], respectively).…”

Section: Resultsmentioning

confidence: 99%

“…Nine papers [ 31 , 48 , 52 , 54 , 65 , 66 , 73 , 75 , 77 ] dealt with porcine models. In all these studies, the number of enrolled animals was very low (1, 10, 11, 7, 1, 1, 1, 5 and 2 pigs in [ 31 , 48 , 52 , 54 , 65 , 66 , 73 , 75 , 77 ], respectively). The small sample size of studies on medium-sized animals could be partly due to the challenges involved in managing experiments, such as costs and housing spaces, equipment, etc., despite the cardiovascular anatomy and physiology of pigs being closer to that of humans [ 84 ].…”

Section: Resultsmentioning

confidence: 99%

“…Seven papers [ 43 , 48 , 54 , 64 , 73 , 77 , 83 ] proposed DL models designed for segmentation tasks. Three of them focused on the left ventricle [ 43 , 48 , 64 ], one at segmenting the whole heart during cardiac arrest [ 54 ] and another on segmenting both the epicardium and endocardium [ 83 ].…”

Section: Resultsmentioning

confidence: 99%

See 4 more Smart Citations

Applications of Deep Learning Algorithms to Ultrasound Imaging Analysis in Preclinical Studies on In Vivo Animals

De Rosa,

L’Abbate,

Kusmic

et al. 2023

View full text Add to dashboard Cite

Background and Aim: Ultrasound (US) imaging is increasingly preferred over other more invasive modalities in preclinical studies using animal models. However, this technique has some limitations, mainly related to operator dependence. To overcome some of the current drawbacks, sophisticated data processing models are proposed, in particular artificial intelligence models based on deep learning (DL) networks. This systematic review aims to overview the application of DL algorithms in assisting US analysis of images acquired in in vivo preclinical studies on animal models. Methods: A literature search was conducted using the Scopus and PubMed databases. Studies published from January 2012 to November 2022 that developed DL models on US images acquired in preclinical/animal experimental scenarios were eligible for inclusion. This review was conducted according to PRISMA guidelines. Results: Fifty-six studies were enrolled and classified into five groups based on the anatomical district in which the DL models were used. Sixteen studies focused on the cardiovascular system and fourteen on the abdominal organs. Five studies applied DL networks to images of the musculoskeletal system and eight investigations involved the brain. Thirteen papers, grouped under a miscellaneous category, proposed heterogeneous applications adopting DL systems. Our analysis also highlighted that murine models were the most common animals used in in vivo studies applying DL to US imaging. Conclusion: DL techniques show great potential in terms of US images acquired in preclinical studies using animal models. However, in this scenario, these techniques are still in their early stages, and there is room for improvement, such as sample sizes, data preprocessing, and model interpretability.

“…Following the review of the title and abstract and, upon necessity, the full text further 187 records were rejected. After the reviewing process, a total of 36 original papers [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 ] and 20 conference proceedings [ 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 ,…”

Section: Resultsmentioning

confidence: 99%

“…Among the 56 selected papers, 16 (28.6%) proposed the application of DL to US images in cardiovascular studies. In particular, these papers focused on the heart [ 43 , 45 , 48 , 54 , 64 , 73 , 83 ], atherosclerotic plaque [ 34 ], carotid [ 56 ]/abdominal [ 40 ]/femoral [ 31 , 52 , 75 ] arteries, femoral veins [ 65 , 66 ] and inferior vena cava [ 77 ].…”

Section: Resultsmentioning

confidence: 99%

“…Nine papers [ 31 , 48 , 52 , 54 , 65 , 66 , 73 , 75 , 77 ] dealt with porcine models. In all these studies, the number of enrolled animals was very low (1, 10, 11, 7, 1, 1, 1, 5 and 2 pigs in [ 31 , 48 , 52 , 54 , 65 , 66 , 73 , 75 , 77 ], respectively).…”

Section: Resultsmentioning

confidence: 99%

“…Nine papers [ 31 , 48 , 52 , 54 , 65 , 66 , 73 , 75 , 77 ] dealt with porcine models. In all these studies, the number of enrolled animals was very low (1, 10, 11, 7, 1, 1, 1, 5 and 2 pigs in [ 31 , 48 , 52 , 54 , 65 , 66 , 73 , 75 , 77 ], respectively). The small sample size of studies on medium-sized animals could be partly due to the challenges involved in managing experiments, such as costs and housing spaces, equipment, etc., despite the cardiovascular anatomy and physiology of pigs being closer to that of humans [ 84 ].…”

Section: Resultsmentioning

confidence: 99%

“…Seven papers [ 43 , 48 , 54 , 64 , 73 , 77 , 83 ] proposed DL models designed for segmentation tasks. Three of them focused on the left ventricle [ 43 , 48 , 64 ], one at segmenting the whole heart during cardiac arrest [ 54 ] and another on segmenting both the epicardium and endocardium [ 83 ].…”

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Applications of Deep Learning Algorithms to Ultrasound Imaging Analysis in Preclinical Studies on In Vivo Animals

De Rosa,

L’Abbate,

Kusmic

et al. 2023

View full text Add to dashboard Cite

Background and Aim: Ultrasound (US) imaging is increasingly preferred over other more invasive modalities in preclinical studies using animal models. However, this technique has some limitations, mainly related to operator dependence. To overcome some of the current drawbacks, sophisticated data processing models are proposed, in particular artificial intelligence models based on deep learning (DL) networks. This systematic review aims to overview the application of DL algorithms in assisting US analysis of images acquired in in vivo preclinical studies on animal models. Methods: A literature search was conducted using the Scopus and PubMed databases. Studies published from January 2012 to November 2022 that developed DL models on US images acquired in preclinical/animal experimental scenarios were eligible for inclusion. This review was conducted according to PRISMA guidelines. Results: Fifty-six studies were enrolled and classified into five groups based on the anatomical district in which the DL models were used. Sixteen studies focused on the cardiovascular system and fourteen on the abdominal organs. Five studies applied DL networks to images of the musculoskeletal system and eight investigations involved the brain. Thirteen papers, grouped under a miscellaneous category, proposed heterogeneous applications adopting DL systems. Our analysis also highlighted that murine models were the most common animals used in in vivo studies applying DL to US imaging. Conclusion: DL techniques show great potential in terms of US images acquired in preclinical studies using animal models. However, in this scenario, these techniques are still in their early stages, and there is room for improvement, such as sample sizes, data preprocessing, and model interpretability.

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Product

Browser Extension Assistant by scite Citation Statement Search Reference Check Visualizations Dashboards Explore Journals Explore Organizations Explore Funders Embedding Badge Embedding Citation Search Pricing

Resources

Blog Help & FAQ Accessibility Statement API Terms For Universities & Governments For Researchers For Publishers For Corporate, Pharma & Enterprise Author Marketing Become an Affiliate Get an organization trial or quote scite Data & Services

About

News & Press Careers Read our Paper Coverage

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Copyright © 2025 scite LLC. All rights reserved.

Made with 💙 for researchers

Part of the Research Solutions Family.