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DC poleHodnotaJazyk
dc.contributor.authorVan Linh, Le
dc.contributor.authorSaut, Olivier
dc.contributor.editorSkala, Václav
dc.date.accessioned2023-10-15T17:03:39Z
dc.date.available2023-10-15T17:03:39Z
dc.date.issued2023
dc.identifier.citationWSCG 2023: full papers proceedings: 1. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 36-42.en
dc.identifier.isbn978-80-86943-32-9
dc.identifier.issn2464–4617 (print)
dc.identifier.issn2464–4625 (CD/DVD)
dc.identifier.urihttp://hdl.handle.net/11025/54397
dc.format7 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.subjectsegmentace pliccs
dc.subjectNSCLCcs
dc.subjectnemalobuněčný karcinom pliccs
dc.subjectkoordinacecs
dc.subjecthluboké učenícs
dc.titleCoordinate-Unet 3D for segmentation of lung parenchymaen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedLung segmentation is an initial step to provide accurate lung parenchyma in many studies on lung diseases based on analyzing the Computed Tomography (CT) scan, especially in Non-Small Cell Lung Cancer (NSCLC) detec tion. In this work, Coordinate-UNet 3D, a model inspired by UNet, is proposed to improve the accuracy of lung segmentation in the CT scan. Like UNet, the proposed model consists of a contracting/encoder path to extract the high-level information and an expansive/decoder path to recover the features to provide the segmentation. However, we have considered modifying the structure inside each level of the model and using the Coordinate Convolutional layer as the final layer to provide the segmentation. This network was trained end-to-end by using a small set of CT scans of NSCLC patients. The experimental results show the proposed network can provide a highly accurate segmentation for the validation set with a Dice Coefficient index of 0.991, an F1 score of 0.976, and a Jaccard index (IOU) of 0.9535.en
dc.subject.translatedlung segmentationen
dc.subject.translatedNSCLCen
dc.subject.translatedNon-Small Cell Lung Canceren
dc.subject.translatedcoordinate convolutionalen
dc.subject.translateddeep learningen
dc.identifier.doihttps://www.doi.org/10.24132/CSRN.3301.6
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:WSCG 2023: Full Papers Proceedings

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