Back to Search Start Over

Multi-level Residual Dual Attention Network for Major Cerebral Arteries Segmentation in MRA towards Diagnosis of Cerebrovascular Disorders

Authors :
Pal, Subhash Chandra
Toumpanakis, Dimitrios
Wikström, Johan
Ahuja, Chirag Kamal
Strand, Robin
Dhara, Ashis Kumar
Pal, Subhash Chandra
Toumpanakis, Dimitrios
Wikström, Johan
Ahuja, Chirag Kamal
Strand, Robin
Dhara, Ashis Kumar
Publication Year :
2024

Abstract

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.

Details

Database :
OAIster
Notes :
English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1399993177
Document Type :
Electronic Resource
Full Text :
https://doi.org/10.1109.tnb.2023.3298444