Literature DB >> 23746488

Localizing target structures in ultrasound video - a phantom study.

R Kwitt1, N Vasconcelos, S Razzaque, S Aylward.   

Abstract

The problem of localizing specific anatomic structures using ultrasound (US) video is considered. This involves automatically determining when an US probe is acquiring images of a previously defined object of interest, during the course of an US examination. Localization using US is motivated by the increased availability of portable, low-cost US probes, which inspire applications where inexperienced personnel and even first-time users acquire US data that is then sent to experts for further assessment. This process is of particular interest for routine examinations in underserved populations as well as for patient triage after natural disasters and large-scale accidents, where experts may be in short supply. The proposed localization approach is motivated by research in the area of dynamic texture analysis and leverages several recent advances in the field of activity recognition. For evaluation, we introduce an annotated and publicly available database of US video, acquired on three phantoms. Several experiments reveal the challenges of applying video analysis approaches to US images and demonstrate that good localization performance is possible with the proposed solution.
Copyright © 2013 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Dynamic textures; Ultrasound imaging; Video analysis

Mesh:

Year:  2013        PMID: 23746488      PMCID: PMC3737575          DOI: 10.1016/j.media.2013.05.003

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  4 in total

1.  Modeling, clustering, and segmenting video with mixtures of dynamic textures.

Authors:  Antoni B Chan; Nuno Vasconcelos
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  2008-05       Impact factor: 6.226

2.  Surgical gesture classification from video data.

Authors:  Benjamín Béjar Haro; Luca Zappella; René Vidal
Journal:  Med Image Comput Comput Assist Interv       Date:  2012

3.  Recognition in ultrasound videos: where am I?

Authors:  Roland Kwitt; Nuno Vasconcelos; Sharif Razzaque; Stephen Aylward
Journal:  Med Image Comput Comput Assist Interv       Date:  2012

Review 4.  Focused Assessment with Sonography for Trauma (FAST): results from an international consensus conference.

Authors:  T M Scalea; A Rodriguez; W C Chiu; F D Brenneman; W F Fallon; K Kato; M G McKenney; M L Nerlich; M G Ochsner; H Yoshii
Journal:  J Trauma       Date:  1999-03
  4 in total
  3 in total

1.  SLIDE: automatic spine level identification system using a deep convolutional neural network.

Authors:  Jorden Hetherington; Victoria Lessoway; Vit Gunka; Purang Abolmaesumi; Robert Rohling
Journal:  Int J Comput Assist Radiol Surg       Date:  2017-03-30       Impact factor: 2.924

2.  Artificial Intelligence to Automatically Assess Scan Quality in Hip Ultrasound.

Authors:  Abhilash Rakkundeth Hareendranathan; Baljot S Chahal; Dornoosh Zonoobi; Dulai Sukhdeep; Jacob L Jaremko
Journal:  Indian J Orthop       Date:  2021-07-17       Impact factor: 1.033

3.  Automatic Recognition of Fetal Facial Standard Plane in Ultrasound Image via Fisher Vector.

Authors:  Baiying Lei; Ee-Leng Tan; Siping Chen; Liu Zhuo; Shengli Li; Dong Ni; Tianfu Wang
Journal:  PLoS One       Date:  2015-05-01       Impact factor: 3.240

  3 in total

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