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dc.contributor.authorSui, Dong
dc.contributor.authorHuang, Zixuan
dc.contributor.authorSong, Xinwei
dc.contributor.authorZhang, Yue
dc.contributor.authorWang, Yantao
dc.contributor.authorZhang, Lei
dc.date.accessioned2021-03-24T16:55:41Z
dc.date.available2021-03-24T16:55:41Z
dc.date.issued2021-01-27
dc.identifier.urihttp://hdl.handle.net/10713/15027
dc.description.abstractBackground analysis of breast cancer can depict the progress and states of the tumour, which is based on the whole breast segmentation from MRI images. The focus of this paper is to construct a pipeline for breast region segmentation for the possibility of breast cancer automatic diagnosis by using MRI image serials. Studies of breast region segmentation based on traditional and deep learning methods have undergone several years, but most of them have not achieved a satisfactory consequence for the following background analysis. In this paper, we proposed a novel pipeline for whole breast region segmentation method based on U-net++, that can achieve a better result compared with the traditional U-net model which is the most common used medical image analysis model and achieve a better IoU than CNN models. We have evaluated the U-net++ model with tradition U-net, our experiments demonstrate that the U-net++ with deep supervision achieves a higher IoU over U-net model.en_US
dc.description.urihttps://doi.org/10.1088/1742-6596/1748/4/042058en_US
dc.language.isoenen_US
dc.publisherIOP Publishing Ltden_US
dc.relation.ispartofJournal of Physics: Conference Seriesen_US
dc.subjectbreast region segmentationen_US
dc.subjectDCE-MRIen_US
dc.subjectU-net++en_US
dc.subject.lcshBreast--Canceren_US
dc.subject.meshDiagnostic Imaging--methodsen_US
dc.titleBreast Regions Segmentation Based on U-net++ from DCE-MRI Image Sequencesen_US
dc.typeArticleen_US
dc.identifier.doi10.1088/1742-6596/1748/4/042058
dc.source.volume1748
dc.source.issue4


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