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dc.contributor.authorJiřík, Miroslav
dc.contributor.authorHácha, Filip
dc.contributor.authorGruber, Ivan
dc.contributor.authorPálek, Richard
dc.contributor.authorMírka, Hynek
dc.contributor.authorŽelezný, Miloš
dc.contributor.authorLiška, Václav
dc.date.accessioned2022-02-28T11:00:22Z-
dc.date.available2022-02-28T11:00:22Z-
dc.date.issued2021
dc.identifier.citationJIŘÍK, M. HÁCHA, F. GRUBER, I. PÁLEK, R. MÍRKA, H. ŽELEZNÝ, M. LIŠKA, V. Why Use Position Features in Liver Segmentation Performed by Convolutional Neural Network. Frontiers in Physiology, 2021, roč. 12, č. October 2021, s. nestránkováno. ISSN: 1664-042Xcs
dc.identifier.issn1664-042X
dc.identifier.uri2-s2.0-85117256040
dc.identifier.urihttp://hdl.handle.net/11025/47013
dc.format9 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherFrontiers Media S.A.en
dc.relation.ispartofseriesFrontiers in Physiologyen
dc.rights© authorsen
dc.titleWhy Use Position Features in Liver Segmentation Performed by Convolutional Neural Networken
dc.title.alternativeProč vvyužívat polohové příznaky při segmentaci jater s využitím konvolučních neuronových sítícs
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThe calculation of liver volume is primarily based on Computed Tomography. Unfortunately, automatic segmentation algorithms based on handcrafted features tend to leak segmented objects into surrounding tissues like the heart or the spleen. Currently, convolutional neural networks are widely used in various applications of computer vision including image segmentation, while providing very promising results. In our work, we utilize robustly segmentable structures like the spine, body surface, and sagittal plane. They are used as key points for position estimation inside the body. The signed distance fields derived from these structures are calculated and used as an additional channel on the input of our convolutional neural network, to be more specific U-Net, which is widely used in medical image segmentation tasks. Our work shows that this additional position information improves the results of the segmentation. We test our approach in two experiments on two public datasets of Computed Tomography images. To evaluate the results, we use the Accuracy, the Hausdorff distance, and the Dice coefficient. Code is publicly available at: https://gitlab.com/hachaf/liver-segmentation.git.en
dc.subject.translatedliver volumetryen
dc.subject.translatedsemantic segmentationen
dc.subject.translatedmachine learningen
dc.subject.translatedconvolutional neural networken
dc.subject.translatedmedical imagingen
dc.subject.translatedposition featuresen
dc.identifier.doi10.3389/fphys.2021.734217
dc.type.statusPeer-revieweden
dc.identifier.document-number710484200001
dc.identifier.obd43933801
dc.project.IDLO1506/PUNTIS - Podpora udržitelnosti centra NTIS - Nové technologie pro informační společnostcs
dc.project.IDLM2015042/E-infrastruktura CESNETcs
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Please use this identifier to cite or link to this item: http://hdl.handle.net/11025/47013

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