Vein segmentation and visualization of upper and lower extremities using convolution neural network.
| Autor: | Laddi A; Biomedical Applications Group, CSIR-Central Scientific Instruments Organisation (CSIO), Chandigarh-160030, India.; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh- 201 002, India., Goyal S; Biomedical Applications Group, CSIR-Central Scientific Instruments Organisation (CSIO), Chandigarh-160030, India.; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh- 201 002, India., Himani, Savlania A; Department of General Surgery, 29751 PGIMER , Chandigarh, India. |
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| Jazyk: | angličtina |
| Zdroj: | Biomedizinische Technik. Biomedical engineering [Biomed Tech (Berl)] 2024 Apr 24; Vol. 69 (5), pp. 455-464. Date of Electronic Publication: 2024 Apr 24 (Print Publication: 2024). |
| DOI: | 10.1515/bmt-2023-0331 |
| Abstrakt: | 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. (© 2024 Walter de Gruyter GmbH, Berlin/Boston.) |
| Databáze: | MEDLINE |
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