Subhash Chandra Pal;Dimitrios Toumpanakis;Johan Wikström;Chirag Kamal Ahuja;Robin Strand;Ashis Kumar Dhara
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引用次数: 0
Abstract
Segmentation of major brain vessels is very important for the diagnosis of cerebrovascular disorders and subsequent surgical planning. Vessel segmentation is an important preprocessing 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.
期刊介绍:
The IEEE Transactions on NanoBioscience reports on original, innovative and interdisciplinary work on all aspects of molecular systems, cellular systems, and tissues (including molecular electronics). Topics covered in the journal focus on a broad spectrum of aspects, both on foundations and on applications. Specifically, methods and techniques, experimental aspects, design and implementation, instrumentation and laboratory equipment, clinical aspects, hardware and software data acquisition and analysis and computer based modelling are covered (based on traditional or high performance computing - parallel computers or computer networks).