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. 2024 Dec 28;14(1):30835.
doi: 10.1038/s41598-024-81703-5.

Innovative modified-net architecture: enhanced segmentation of deep vein thrombosis

Pavihaa Lakshmi B #  1 , Vidhya S #  2
Affiliations

Innovative modified-net architecture: enhanced segmentation of deep vein thrombosis

Pavihaa Lakshmi B et al. Sci Rep. .

Abstract

A new era for diagnosing and treating Deep Vein Thrombosis (DVT) relies on precise segmentation from medical images. Our research introduces a novel algorithm, the Modified-Net architecture, which integrates a broad spectrum of architectural components tailored to detect the intricate patterns and variances in DVT imaging data. Our work integrates advanced components such as dilated convolutions for larger receptive fields, spatial pyramid pooling for context, residual and inception blocks for multiscale feature extraction, and attention mechanisms for highlighting key features. Our framework enhances precision of DVT region identification, attaining an accuracy of 98.92%, with a loss of 0.0269. The model also validates sensitivity 96.55%, specificity 96.70%, precision 98.61%, dice 97.48% and Intersection over Union (IoU) 95.10% offering valuable insights into DVT segmentation. Our framework significantly improves segmentation performance over traditional methods such as Convolutional Neural Network , Sequential, U-Net, Schematic. The management of DVT can be improved through enhanced segmentation techniques, which can improve clinical observation, treatment planning, and ultimately patient outcomes.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Anatomical representation of DVT occurrence and PE development.
Fig. 2
Fig. 2
The workflow of modified-Net architecture model.
Fig. 3
Fig. 3
Graphical representation of performance metric: accuracy and loss.
Fig. 4
Fig. 4
Graphical representation of performance metric: precision, sensitivity, and specificity.
Fig. 5
Fig. 5
Comparative summary between our proposed model and traditional techniques.
See this image and copyright information in PMC

References

    1. Schick, M. A. & Pacifico, L. Deep venous thrombosis of the lower extremity. In StatPearls [Internet] (StatPearls Publishing, 2022). - PubMed
    1. Qureshi, I. et al. Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends. Information Fusion90, 316–352 (2023).
    1. Chen, Z. Medical image segmentation based on u-net. In Journal of Physics: Conference Series, vol. 2547, 012010 (IOP Publishing, 2023).
    1. Waheed, S. M., Kudaravalli, P. & Hotwagner, D. T. Deep vein thrombosis. (2018). - PubMed
    1. Zhang, Z. et al. Point-of-care testing in the diagnosis of deep vein thrombosis: A review. IEEE Systems, Man, and Cybernetics Magazine9, 49–56 (2023).

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