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Multi-level Residua...
Multi-level Residual Dual Attention Network for Major Cerebral Arteries Segmentation in MRA towards Diagnosis of Cerebrovascular Disorders
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- Pal, Subhash Chandra (författare)
- Department of Electrical Engineering, National Institute of Technology Durgapur, India
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- Toumpanakis, Dimitrios (författare)
- Uppsala universitet,Radiologi
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- Wikström, Johan, Professor, 1964- (författare)
- Uppsala universitet,Radiologi
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- Ahuja, Chirag Kamal (författare)
- Department of Radio Diagnosis and Imaging, PGIMER, Chandigarh, INDIA
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- Strand, Robin, 1978- (författare)
- Uppsala universitet,Avdelningen för visuell information och interaktion,Bildanalys och människa-datorinteraktion
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- Dhara, Ashis Kumar (författare)
- Department of Electrical Engineering, National Institute of Technology Durgapur, India
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(creator_code:org_t)
- IEEE, 2024
- 2024
- Engelska.
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Ingår i: IEEE Transactions on Nanobioscience. - : IEEE. - 1536-1241 .- 1558-2639. ; 23:1, s. 167-175
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Segmentation of major brain vessels is very important for the diagnosis of cerebrovascular disorders and subsequent surgical planning. Vessel segmentation is an important pre-processing step for a wide range of algorithms for the automatic diagnosis or treatment of several vascular pathologies and as such, it is valuable to have a well-performing vascular segmentation pipeline. In this article, we propose an end-to-end multiscale residual dual attention deep neural network for resilient major brain vessel segmentation. In the proposed network, the encoder and decoder blocks of the U-Net are replaced with the multi-level atrous residual blocks to enhance the learning capability by increasing the receptive field to extract the various semantic coarse- and fine- grained features. Dual attention block is incorporated in the bottleneck to perform effective multiscale information fusion to obtain detailed structure of blood vessels. The methods were evaluated on the publicly available TubeTK data set. The proposed method outperforms the state-of-the-art techniques with dice of 0.79 on the whole-brain prediction. The statistical and visual assessments indicate that proposed network is robust to outliers and maintains higher consistency in vessel continuity than the traditional U-Net and its variations.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
Nyckelord
- Computerized Image Processing
- Datoriserad bildbehandling
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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