Deep learning based ultrasonic visualization of distal humeral cartilage for image-guided therapy: a pilot validation study.
Deep learning
distal humeral cartilage
image-guided therapy (IGT)
minimally invasive surgery
ultrasound visualization
Journal
Quantitative imaging in medicine and surgery
ISSN: 2223-4292
Titre abrégé: Quant Imaging Med Surg
Pays: China
ID NLM: 101577942
Informations de publication
Date de publication:
01 Aug 2023
01 Aug 2023
Historique:
received:
03
01
2023
accepted:
26
05
2023
medline:
15
8
2023
pubmed:
15
8
2023
entrez:
15
8
2023
Statut:
ppublish
Résumé
Ultrasound is widely used for image-guided therapy (IGT) in many surgical fields, thanks to its various advantages, such as portability, lack of radiation and real-time imaging. This article presents the first attempt to utilize multiple deep learning algorithms in distal humeral cartilage segmentation for dynamic, volumetric ultrasound images employed in minimally invasive surgery. The dataset, consisting 5,321 ultrasound images were collected from 12 healthy volunteers. These images were randomly split into training and validation sets in an 8:2 ratio. Based on deep learning algorithms, 9 semantic segmentation networks were developed and trained using our dataset at Southern University of Science and Technology Hospital in September 2022. The performance of the networks was evaluated based on their segmenting accuracy and processing efficiency. Furthermore, these networks were implemented in an IGT system to assess their feasibility in 3-dimentional imaging precision. In 2D segmentation, Medical Transformer (MedT) showed the highest accuracy result with a Dice score of 89.4%, however, the efficiency in processing images was relatively lower at 2.6 frames per second (FPS). In 3D imaging, the average root mean square (RMS) between ultrasound (US)-generated models based on the networks and magnetic resonance imaging (MRI)-generated models was no more than 1.12 mm. The findings of this study indicate the technological feasibility of a novel method for real-time visualization of distal humeral cartilage. The increased precision of ultrasound calibration and segmentation are both important approaches to improve the accuracy of 3D imaging.
Sections du résumé
Background
UNASSIGNED
Ultrasound is widely used for image-guided therapy (IGT) in many surgical fields, thanks to its various advantages, such as portability, lack of radiation and real-time imaging. This article presents the first attempt to utilize multiple deep learning algorithms in distal humeral cartilage segmentation for dynamic, volumetric ultrasound images employed in minimally invasive surgery.
Methods
UNASSIGNED
The dataset, consisting 5,321 ultrasound images were collected from 12 healthy volunteers. These images were randomly split into training and validation sets in an 8:2 ratio. Based on deep learning algorithms, 9 semantic segmentation networks were developed and trained using our dataset at Southern University of Science and Technology Hospital in September 2022. The performance of the networks was evaluated based on their segmenting accuracy and processing efficiency. Furthermore, these networks were implemented in an IGT system to assess their feasibility in 3-dimentional imaging precision.
Results
UNASSIGNED
In 2D segmentation, Medical Transformer (MedT) showed the highest accuracy result with a Dice score of 89.4%, however, the efficiency in processing images was relatively lower at 2.6 frames per second (FPS). In 3D imaging, the average root mean square (RMS) between ultrasound (US)-generated models based on the networks and magnetic resonance imaging (MRI)-generated models was no more than 1.12 mm.
Conclusions
UNASSIGNED
The findings of this study indicate the technological feasibility of a novel method for real-time visualization of distal humeral cartilage. The increased precision of ultrasound calibration and segmentation are both important approaches to improve the accuracy of 3D imaging.
Identifiants
pubmed: 37581069
doi: 10.21037/qims-23-9
pii: qims-13-08-5306
pmc: PMC10423345
doi:
Types de publication
Journal Article
Langues
eng
Pagination
5306-5320Informations de copyright
2023 Quantitative Imaging in Medicine and Surgery. All rights reserved.
Déclaration de conflit d'intérêts
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-23-9/coif). The authors have no conflicts of interest to declare.
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