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dc.title | ISLES challenge: U-shaped convolution neural network with dilated convolution for 3D stroke lesion segmentation | en |
dc.contributor.author | Turečková, Alžběta | |
dc.contributor.author | Rodríguez-Sánchez, Antonio Jose | |
dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | |
dc.identifier.issn | 0302-9743 Scopus Sources, Sherpa/RoMEO, JCR | |
dc.identifier.isbn | 978-3-03-011722-1 | |
dc.date.issued | 2019 | |
utb.relation.volume | 11383 LNCS | |
dc.citation.spage | 319 | |
dc.citation.epage | 327 | |
dc.event.title | 4th International MICCAI Brainlesion Workshop, BrainLes 2018 held in conjunction with the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2018 | |
dc.event.location | Granada | |
utb.event.state-en | Spain | |
utb.event.state-cs | Španělsko | |
dc.event.sdate | 2018-09-16 | |
dc.event.edate | 2018-09-20 | |
dc.type | conferenceObject | |
dc.language.iso | en | |
dc.publisher | Springer Verlag | |
dc.identifier.doi | 10.1007/978-3-030-11723-8_32 | |
dc.subject | medical image segmentation | en |
dc.subject | deep convolutional neural networks | en |
dc.subject | U-Net | en |
dc.subject | dilated convolution | en |
dc.description.abstract | In this paper, we propose the algorithm for stroke lesion segmentation based on a deep convolutional neural network (CNN). The model is based on U-shaped CNN, which has been applied successfully to other medical image segmentation tasks. The network architecture was derived from the model presented in Isensee et al. [1] and is capable of processing whole 3D images. The model incorporates the convolution layers through upsampled filters – also known as dilated convolution. This change enlarges filter’s field of the view and allows the net to integrate larger context into the computation. We add the dilated convolution into different parts of network architecture and study the impact on the overall model performance. The best model which uses the dilated convolution in the input of the net outperforms the original architecture in nearly all used evaluation metrics. The code and trained models can be found on the GitHub website: http://github.com/tureckova/ISLES2018/. © Springer Nature Switzerland AG 2019. | en |
utb.faculty | Faculty of Applied Informatics | |
dc.identifier.uri | http://hdl.handle.net/10563/1008607 | |
utb.identifier.obdid | 43879270 | |
utb.identifier.scopus | 2-s2.0-85063451535 | |
utb.identifier.wok | 000612997600032 | |
utb.source | d-scopus | |
dc.date.accessioned | 2019-07-08T11:59:58Z | |
dc.date.available | 2019-07-08T11:59:58Z | |
dc.description.sponsorship | Internal Grant Agency of Tomas Bata University [IGA/CebiaTech/2018/003] | |
utb.contributor.internalauthor | Turečková, Alžběta | |
utb.fulltext.affiliation | Alzbeta Tureckova 1, Antonio J. Rodríguez-Sánchez 2 1 Faculty of Applied Informatics, Tomas Bata University in Zlin, Nam T.G. Masaryka 5555, 760 01 Zlin, Czech Republic tureckova@utb.cz 2 Intelligent and Interactive Systems, Institute of Computer Science, University of Innsbruck, Innsbruck, Austria | |
utb.fulltext.dates | - | |
utb.wos.affiliation | [Tureckova, Alzbeta] Tomas Bata Univ Zlin, Fac Appl Informat, Nam TG Masaryka 5555, Zlin 76001, Czech Republic; [Rodriguez-Sanchez, Antonio J.] Univ Innsbruck, Inst Comp Sci, Intelligent & Interact Syst, Innsbruck, Austria | |
utb.scopus.affiliation | Faculty of Applied Informatics, Tomas Bata University in Zlin, Nam T.G. Masaryka 5555, Zlin, 760 01, Czech Republic; Intelligent and Interactive Systems, Institute of Computer Science, University of Innsbruck, Innsbruck, Austria | |
utb.fulltext.faculty | Faculty of Applied Informatics |