Hierarchical 3D Feature Learning forPancreas Segmentation

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dc.authorscopusid57226020618
dc.authorscopusid57221158503
dc.authorscopusid57203681040
dc.authorscopusid57223706450
dc.authorscopusid24176491700
dc.authorscopusid23391134800
dc.contributor.authorProietto Salanitri F.
dc.contributor.authorBellitto G.
dc.contributor.authorIrmakci I.
dc.contributor.authorPalazzo S.
dc.contributor.authorBagci U.
dc.contributor.authorSpampinato C.
dc.date.accessioned2023-01-12T20:22:22Z
dc.date.available2023-01-12T20:22:22Z
dc.date.issued2021
dc.departmentN/A/Departmenten_US
dc.description12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 -- 27 September 2021 through 27 September 2021 -- -- 266089en_US
dc.description.abstractWe propose a novel 3D fully convolutional deep network for automated pancreas segmentation from both MRI and CT scans. More specifically, the proposed model consists of a 3D encoder that learns to extract volume features at different scales; features taken at different points of the encoder hierarchy are then sent to multiple 3D decoders that individually predict intermediate segmentation maps. Finally, all segmentation maps are combined to obtain a unique detailed segmentation mask. We test our model on both CT and MRI imaging data: the publicly available NIH Pancreas-CT dataset (consisting of 82 contrast-enhanced CTs) and a private MRI dataset (consisting of 40 MRI scans). Experimental results show that our model outperforms existing methods on CT pancreas segmentation, obtaining an average Dice score of about 88%, and yields promising segmentation performance on a very challenging MRI data set (average Dice score is about 77%). Additional control experiments demonstrate that the achieved performance is due to the combination of our 3D fully-convolutional deep network and the hierarchical representation decoding, thus substantiating our architectural design. © 2021, Springer Nature Switzerland AG.en_US
dc.identifier.doi10.1007/978-3-030-87589-3_25
dc.identifier.endpage247en_US
dc.identifier.isbn9783030875886
dc.identifier.issn03029743
dc.identifier.issn0302-9743en_US
dc.identifier.scopus2-s2.0-85116503336en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage238en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-030-87589-3_25
dc.identifier.urihttps://hdl.handle.net/11454/79430
dc.identifier.volume12966 LNCSen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCT and MRI pancreas segmentationen_US
dc.subjectFully convolutional neural networksen_US
dc.subjectHierarchical encoder-decoder architectureen_US
dc.subject3D modelingen_US
dc.subjectComputerized tomographyen_US
dc.subjectConvolutionen_US
dc.subjectConvolutional neural networksen_US
dc.subjectDecodingen_US
dc.subjectMachine learningen_US
dc.subjectMedical computingen_US
dc.subjectMedical imagingen_US
dc.subjectNetwork codingen_US
dc.subjectStatistical testsen_US
dc.subjectConvolutional neural networken_US
dc.subjectCT and MRI pancreas segmentationen_US
dc.subjectCT-scanen_US
dc.subjectEncoder-decoder architectureen_US
dc.subjectFeature learningen_US
dc.subjectFully convolutional neural networken_US
dc.subjectHierarchical encoder-decoder architectureen_US
dc.subjectLearn+en_US
dc.subjectMRI scanen_US
dc.subjectSegmentation mapen_US
dc.subjectMagnetic resonance imagingen_US
dc.titleHierarchical 3D Feature Learning forPancreas Segmentationen_US
dc.typeConference Objecten_US

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