W-RSAUnet: Wavelet Integrated Residual Spatial Attention with Adaptive  Spectral Patching for Distal Airway Extraction

P. Sutha,Dr. R. Shenbagavalli

W-RSAUnet: Wavelet Integrated Residual Spatial Attention with Adaptive Spectral Patching for Distal Airway Extraction

Authors

P. Sutha,Dr. R. Shenbagavalli

Keywords:

Airway Segmentation, Wavelet, Spectral Patching, Spatial Attention Gating, Temperature Factor

Abstract

Automated 3D pulmonary airway segmentation remains a formidable challenge due tothe exponential thinning of distal branches and the extreme sparsity of bronchial structureswithin high-resolution CT volumes. Standard deep learning architectures are plagued with
feature erosion during downsampling, resulting in fractured tree topologies. In this work, we propose W-RSAUnet, a dual-domain learning framework

References

[1] Michael Jaeger, J., Brian J. Titus, & Randal S. Blank. (2019). Essential anatomy and physiology of the respiratory system and the pulmonary circulation. In Principles and Practice of Anesthesia for Thoracic Surgery. Cham: Springer International Publishing, Pages 65–92.

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Published

2024-07-20

How to Cite

P. Sutha,Dr. R. Shenbagavalli. (2024). W-RSAUnet: Wavelet Integrated Residual Spatial Attention with Adaptive Spectral Patching for Distal Airway Extraction. Journal of Computational Analysis and Applications (JoCAAA), 33(07), 3713–3729. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/5687