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dc.contributor.authorOsadebey, Michael
dc.contributor.authorAndersen, Hilde Kjernlie
dc.contributor.authorWaaler, Dag
dc.contributor.authorFosså, Kristian
dc.contributor.authorMartinsen, Anne Catrine Trægde
dc.contributor.authorPedersen, Marius
dc.date.accessioned2021-09-29T11:08:02Z
dc.date.available2021-09-29T11:08:02Z
dc.date.created2021-08-04T12:22:30Z
dc.date.issued2021-07-15
dc.identifier.issn1471-2342
dc.identifier.urihttps://hdl.handle.net/11250/2785997
dc.description.abstractBackground: Lung region segmentation is an important stage of automated image-based approaches for the diagnosis of respiratory diseases. Manual methods executed by experts are considered the gold standard, but it is time consuming and the accuracy is dependent on radiologists’ experience. Automated methods are relatively fast and reproducible with potential to facilitate physician interpretation of images. However, these benefits are possible only after overcoming several challenges. The traditional methods that are formulated as a three-stage segmentation demonstrate promising results on normal CT data but perform poorly in the presence of pathological features and variations in image quality attributes. The implementation of deep learning methods that can demonstrate superior performance over traditional methods is dependent on the quantity, quality, cost and the time it takes to generate training data. Thus, efficient and clinically relevant automated segmentation method is desired for the diagnosis of respiratory diseases. Methods: We implement each of the three stages of traditional methods using deep learning methods trained on five different configurations of training data with ground truths obtained from the 3D Image Reconstruction for Comparison of Algorithm Database (3DIRCAD) and the Interstitial Lung Diseases (ILD) database. The data was augmented with the Lung Image Database Consortium (LIDC-IDRI) image collection and a realistic phantom. A convolutional neural network (CNN) at the preprocessing stage classifies the input into lung and none lung regions. The processing stage was implemented using a CNN-based U-net while the postprocessing stage utilize another U-net and CNN for contour refinement and filtering out false positives, respectively. Results: The performance of the proposed method was evaluated on 1230 and 1100 CT slices from the 3DIRCAD and ILD databases. We investigate the performance of the proposed method on five configurations of training data and three configurations of the segmentation system; three-stage segmentation and three-stage segmentation without a CNN classifier and contrast enhancement, respectively. The Dice-score recorded by the proposed method range from 0.76 to 0.95. Conclusion: The clinical relevance and segmentation accuracy of deep learning models can improve though deep learning-based three-stage segmentation, image quality evaluation and enhancement as well as augmenting the training data with large volume of cheap and quality training data. We propose a new and novel deep learning-based method of contour refinement.en_US
dc.description.sponsorshipMichael Osadebey was funded by the European Research Consortium on Informatics and Mathematics (ERCIM).en_US
dc.language.isoengen_US
dc.publisherBMCen_US
dc.relation.ispartofseriesBMC Medical Imaging;21, Article number: 112 (2021)
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.subjectComputed tomographyen_US
dc.subjectThree-stage segmentationsen_US
dc.subjectDeep learning networksen_US
dc.subjectLung regionsen_US
dc.subjectLung contoursen_US
dc.titleThree-stage segmentation of lung region from CT images using deep neural networksen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holder© The Author(s) 2021.en_US
dc.source.articlenumber112en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.doihttps://doi.org/10.1186/s12880-021-00640-1
dc.identifier.cristin1923835
dc.source.journalBMC Medical Imagingen_US
dc.source.volume21en_US
dc.source.pagenumber1-19en_US


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal