FEW-VIEW X-RAY CT SEGMENTATION OF THIN TUBULAR STRUCTURES
Issued Date
2025-01-01
Resource Type
Scopus ID
2-s2.0-105035614684
Journal Title
2025 IEEE International Conference on Image Processing Workshops Icipw 2025 Proceedings
Start Page
761
End Page
766
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SCOPUS
Bibliographic Citation
2025 IEEE International Conference on Image Processing Workshops Icipw 2025 Proceedings (2025) , 761-766
Suggested Citation
Fitriyah H., Muneesawang P., Lee I. FEW-VIEW X-RAY CT SEGMENTATION OF THIN TUBULAR STRUCTURES. 2025 IEEE International Conference on Image Processing Workshops Icipw 2025 Proceedings (2025) , 761-766. 766. doi:10.1109/ICIPW68931.2025.11386261 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/116314
Title
FEW-VIEW X-RAY CT SEGMENTATION OF THIN TUBULAR STRUCTURES
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Abstract
X-ray CT scanning is both time-consuming and radiation-intensive due to the large number of projections needed to produce high-quality images. Reducing the number of projections can accelerate acquisition and reduce radiation exposure, but often leads to degraded image quality, making the segmentation of fine-grained structures particularly challenging. To address this few-view imaging challenge, this study proposed a modified 3D U-Net model for segmenting thin tubular structures, with scalability that ensures high accuracy even with reduced input quality. The proposed model integrates residual blocks, squeeze-and-excitation (SE) blocks, and dilated convolutions to enlarge the receptive field and emphasise important channels, without significantly increasing the number of learnable parameters. Training was performed on few-view CT reconstructions generated from images with a dense projection. Experiments conducted in plant root and lung vessel datasets demonstrate that certain enhancements improved segmentation accuracy under severe sparsity.