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Deep Learning in Ultrasound Imaging for Healthcare
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Deep Learning in Ultrasound Imaging for Healthcare

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Artificial Intelligence in Medicine".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 6306

Special Issue Editor

Dr. Francesco Faita

E-Mail Website
Guest Editor
Institute of Clinical Physiology, Italian National Research Council, 56124 Pisa, Italy
Interests: ultrasound; artificial intelligence; cardiovascular

Special Issue Information

Dear Colleagues,

Artificial intelligence is transforming the world and deep learning is the primum movens. This revolution has not spared medical imaging, where deep learning has been used in different tasks, from image formation to image segmentation and classification, improving clinical processes and contributing to a reduction in healthcare costs.

Ultrasound, a native multimodal imaging modality, is becoming an invaluable tool for clinical practitioners due to its unique advantages, such as low cost, portability, large availability, and the absence of ionizing radiation. These features have earned ultrasound the definition of stethoscope of the future.

Accordingly, the contribution of deep learning to ultrasound is of strategic importance as it could improve both efficiency and diffusion of this imaging technique in every healthcare sector.

Following these premises, I would like to invite researchers to contribute their insights and findings in the form of commentaries, original researches, and reviews for this Special Issue, entitled “Deep Learning in Ultrasound Imaging for Healthcare”.

The purpose of this Special Issue is to highlight the benefits of empowering US imaging with the application of deep learning technologies.

Dr. Francesco Faita
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Healthcare is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • ultrasound
  • deep learning
  • artificial intelligence

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Published Papers (2 papers)

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Research

9 pages, 1392 KiB  
Article
Deep Learning on Ultrasound Images Visualizes the Femoral Nerve with Good Precision
by Johan Berggreen, Anders Johansson, John Jahr, Sebastian Möller and Tomas Jansson
Healthcare 2023, 11(2), 184; https://doi.org/10.3390/healthcare11020184 - 7 Jan 2023
Cited by 6 | Viewed by 2355
Abstract
The number of hip fractures per year worldwide is estimated to reach 6 million by the year 2050. Despite the many advantages of regional blockades when managing pain from such a fracture, these are used to a lesser extent than general analgesia. One [...] Read more.
The number of hip fractures per year worldwide is estimated to reach 6 million by the year 2050. Despite the many advantages of regional blockades when managing pain from such a fracture, these are used to a lesser extent than general analgesia. One reason is that the opportunities for training and obtaining clinical experience in applying nerve blocks can be a challenge in many clinical settings. Ultrasound image guidance based on artificial intelligence may be one way to increase nerve block success rate. We propose an approach using a deep learning semantic segmentation model with U-net architecture to identify the femoral nerve in ultrasound images. The dataset consisted of 1410 ultrasound images that were collected from 48 patients. The images were manually annotated by a clinical professional and a segmentation model was trained. After training the model for 350 epochs, the results were validated with a 10-fold cross-validation. This showed a mean Intersection over Union of 74%, with an interquartile range of 0.66–0.81. Full article
(This article belongs to the Special Issue Deep Learning in Ultrasound Imaging for Healthcare)
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19 pages, 5258 KiB  
Article
Bandwidth Improvement in Ultrasound Image Reconstruction Using Deep Learning Techniques
by Navchetan Awasthi, Laslo van Anrooij, Gino Jansen, Hans-Martin Schwab, Josien P. W. Pluim and Richard G. P. Lopata
Healthcare 2023, 11(1), 123; https://doi.org/10.3390/healthcare11010123 - 30 Dec 2022
Cited by 1 | Viewed by 3022
Abstract
Ultrasound (US) imaging is a medical imaging modality that uses the reflection of sound in the range of 2–18 MHz to image internal body structures. In US, the frequency bandwidth (BW) is directly associated with image resolution. BW is a property of the [...] Read more.
Ultrasound (US) imaging is a medical imaging modality that uses the reflection of sound in the range of 2–18 MHz to image internal body structures. In US, the frequency bandwidth (BW) is directly associated with image resolution. BW is a property of the transducer and more bandwidth comes at a higher cost. Thus, methods that can transform strongly bandlimited ultrasound data into broadband data are essential. In this work, we propose a deep learning (DL) technique to improve the image quality for a given bandwidth by learning features provided by broadband data of the same field of view. Therefore, the performance of several DL architectures and conventional state-of-the-art techniques for image quality improvement and artifact removal have been compared on in vitro US datasets. Two training losses have been utilized on three different architectures: a super resolution convolutional neural network (SRCNN), U-Net, and a residual encoder decoder network (REDNet) architecture. The models have been trained to transform low-bandwidth image reconstructions to high-bandwidth image reconstructions, to reduce the artifacts, and make the reconstructions visually more attractive. Experiments were performed for 20%, 40%, and 60% fractional bandwidth on the original images and showed that the improvements obtained are as high as 45.5% in RMSE, and 3.85 dB in PSNR, in datasets with a 20% bandwidth limitation. Full article
(This article belongs to the Special Issue Deep Learning in Ultrasound Imaging for Healthcare)
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