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Vein segmentation and visualization of upper and lower extremities using convolution neural network

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Veröffentlicht/Copyright: 24. April 2024
Biomedical Engineering / Biomedizinische Technik
Aus der Zeitschrift Biomedical Engineering / Biomedizinische Technik Band 69 Heft 5

Abstract

Objectives

The study focused on developing a reliable real-time venous localization, identification, and visualization framework based upon deep learning (DL) self-parametrized Convolution Neural Network (CNN) algorithm for segmentation of the venous map for both lower and upper limb dataset acquired under unconstrained conditions using near-infrared (NIR) imaging setup, specifically to assist vascular surgeons during venipuncture, vascular surgeries, or Chronic Venous Disease (CVD) treatments.

Methods

A portable image acquisition setup has been designed to collect venous data (upper and lower extremities) from 72 subjects. A manually annotated image dataset was used to train and compare the performance of existing well-known CNN-based architectures such as ResNet and VGGNet with self-parameterized U-Net, improving automated vein segmentation and visualization.

Results

Experimental results indicated that self-parameterized U-Net performs better at segmenting the unconstrained dataset in comparison with conventional CNN feature-based learning models, with a Dice score of 0.58 and displaying 96.7 % accuracy for real-time vein visualization, making it appropriate to locate veins in real-time under unconstrained conditions.

Conclusions

Self-parameterized U-Net for vein segmentation and visualization has the potential to reduce risks associated with traditional venipuncture or CVD treatments by outperforming conventional CNN architectures, providing vascular assistance, and improving patient care and treatment outcomes.

Keywords: near infrared imaging; deep learning; CNN architecture; image segmentation; vein visualization
Corresponding author: Amit Laddi, PhD, ME, B.Tech (Hons.), Senior Principal Scientist, Biomedical Applications Group, CSIR-Central Scientific Instruments Organisation (CSIO), Chandigarh 160030, India; and Asst. Professor, Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201 002, Uttar Pradesh, India, E-mail:

Acknowledgments

Authors are thankful to CSIR India for project grants HCP-026 (Task 3.1) and OLP-0271. Authors acknowledge CSIR-Central Scientific Instruments Organisation (CSIO), Chandigarh, India, and Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India, for the infrastructure facilities and support in carrying out this research work.

  1. Research ethics: This study was performed in line with the approvals of the Institutional Ethics Committee (IEC), CSIR-CSIO, India (Ref: IEC/CSIO/2021/09, Dated 25th June 2021).

  2. Informed consent: Informed consent was obtained from all individuals included in this study.

  3. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission. The contribution of each author are summerised as: Shivalika Goyal: Study design, analysis and interpretation of results, manuscript writing/compilation; Himani: Patient data collection, data design, and results; Ajay Savlania: Medical inputs in designing protocol and study validation, manuscript review; Amit Laddi: Study conception, critical review and manuscript editing.

  4. Competing interests: The authors state no conflict of interest.

  5. Research funding: The study and experimental work has been funded by Council of Scientific and Industrial Research (CSIR) under Medical Instruments and Devices Mission (HCP-26-3.1).

  6. Data availability: The raw data can be obtained on request from the corresponding author.

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Received: 2023-07-19
Accepted: 2024-04-03
Published Online: 2024-04-24
Published in Print: 2024-10-28

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Research Articles
  3. Sparse-view CT reconstruction based on group-based sparse representation using weighted guided image filtering
  4. Comparative evaluation of volumetry estimation from plain and contrast enhanced computed tomography liver images
  5. Vein segmentation and visualization of upper and lower extremities using convolution neural network
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https://doi.org/10.1515/bmt-2023-0331
Schlagwörter für diesen Artikel
near infrared imaging; deep learning; CNN architecture; image segmentation; vein visualization

Artikel in diesem Heft

  1. Frontmatter
  2. Research Articles
  3. Sparse-view CT reconstruction based on group-based sparse representation using weighted guided image filtering
  4. Comparative evaluation of volumetry estimation from plain and contrast enhanced computed tomography liver images
  5. Vein segmentation and visualization of upper and lower extremities using convolution neural network
  6. STF-Net: sparsification transformer coding guided network for subcortical brain structure segmentation
  7. Machine learning based endothelial cell image analysis of patients undergoing descemet membrane endothelial keratoplasty surgery
  8. AML leukocyte classification method for small samples based on ACGAN
  9. Deep learning classification of EEG-based BCI monitoring of the attempted arm and hand movements
  10. Hybrid method for noise rejection from breath sound using transient artifact reduction algorithm and spectral subtraction
  11. Optimized Schlieren imaging for real-time visualization of high-intensity focused ultrasound waves
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