Literature DB >> 27727021

Automatic Segmentation and Probe Guidance for Real-Time Assistance of Ultrasound-Guided Femoral Nerve Blocks.

Erik Smistad1, Daniel Høyer Iversen2, Linda Leidig3, Janne Beate Lervik Bakeng4, Kaj Fredrik Johansen5, Frank Lindseth2.   

Abstract

Ultrasound-guided regional anesthesia can be challenging, especially for inexperienced physicians. The goal of the proposed methods is to create a system that can assist a user in performing ultrasound-guided femoral nerve blocks. The system indicates in which direction the user should move the ultrasound probe to investigate the region of interest and to reach the target site for needle insertion. Additionally, the system provides automatic real-time segmentation of the femoral artery, the femoral nerve and the two layers fascia lata and fascia iliaca. This aids in interpretation of the 2-D ultrasound images and the surrounding anatomy in 3-D. The system was evaluated on 24 ultrasound acquisitions of both legs from six subjects. The estimated target site for needle insertion and the segmentations were compared with those of an expert anesthesiologist. Average target distance was 8.5 mm with a standard deviation of 2.5 mm. The mean absolute differences of the femoral nerve and the fascia segmentations were about 1-3 mm. Copyright Â
© 2016 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Assistant; Femoral nerve; Regional anesthesia; Segmentation

Mesh:

Year:  2016        PMID: 27727021     DOI: 10.1016/j.ultrasmedbio.2016.08.036

Source DB:  PubMed          Journal:  Ultrasound Med Biol        ISSN: 0301-5629            Impact factor:   2.998


  5 in total

1.  Highlighting nerves and blood vessels for ultrasound-guided axillary nerve block procedures using neural networks.

Authors:  Erik Smistad; Kaj Fredrik Johansen; Daniel Høyer Iversen; Ingerid Reinertsen
Journal:  J Med Imaging (Bellingham)       Date:  2018-11-10

Review 2.  Artificial Intelligence: Innovation to Assist in the Identification of Sono-anatomy for Ultrasound-Guided Regional Anaesthesia.

Authors:  James Lloyd; Robert Morse; Alasdair Taylor; David Phillips; Helen Higham; David Burckett-St Laurent; James Bowness
Journal:  Adv Exp Med Biol       Date:  2022       Impact factor: 2.622

3.  Attention-VGG16-UNet: a novel deep learning approach for automatic segmentation of the median nerve in ultrasound images.

Authors:  Aiyue Huang; Li Jiang; Jiangshan Zhang; Qing Wang
Journal:  Quant Imaging Med Surg       Date:  2022-06

4.  A real-time anatomy ıdentification via tool based on artificial ıntelligence for ultrasound-guided peripheral nerve block procedures: an accuracy study.

Authors:  Irfan Gungor; Berrin Gunaydin; Suna O Oktar; Beyza M Buyukgebiz; Selin Bagcaz; Miray Gozde Ozdemir; Gozde Inan
Journal:  J Anesth       Date:  2021-05-19       Impact factor: 2.078

5.  Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video.

Authors:  Lior Drukker; Harshita Sharma; Richard Droste; Mohammad Alsharid; Pierre Chatelain; J Alison Noble; Aris T Papageorghiou
Journal:  Sci Rep       Date:  2021-07-08       Impact factor: 4.379

  5 in total

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