Pub Date : 2023-07-24DOI: 10.1109/TNB.2023.3298444
Subhash Chandra Pal;Dimitrios Toumpanakis;Johan Wikström;Chirag Kamal Ahuja;Robin Strand;Ashis Kumar Dhara
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.
{"title":"Multi-Level Residual Dual Attention Network for Major Cerebral Arteries Segmentation in MRA Toward Diagnosis of Cerebrovascular Disorders","authors":"Subhash Chandra Pal;Dimitrios Toumpanakis;Johan Wikström;Chirag Kamal Ahuja;Robin Strand;Ashis Kumar Dhara","doi":"10.1109/TNB.2023.3298444","DOIUrl":"10.1109/TNB.2023.3298444","url":null,"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.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10242076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-11DOI: 10.1109/TNB.2023.3294330
J. Divya;S. Selvendran;A. Sivanantha Raja;Vamsi Borra
A dual-channel D-shaped photonic crystal fiber (PCF) based plasmonic sensor is proposed in this paper for the simultaneous detection of two different analytes using the surface plasmon resonance (SPR) technique. The sensor employs a 50 nm-thick layer of chemically stable gold on both cleaved surfaces of the PCF to induce the SPR effect. This configuration offers superior sensitivity and rapid response, making it highly effective for sensing applications. Numerical investigations are conducted using the finite element method (FEM). After optimizing the structural parameters, the sensor exhibits a maximum wavelength sensitivity of 10000 nm/RIU and an amplitude sensitivity of −216 RIU $^{-{1}}$