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A deep level set method for image segmentation

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dc.contributor.authorTang, Min
dc.contributor.authorValipour, Sepehr
dc.contributor.authorZhang, Zichen
dc.contributor.authorCobzas, Dana
dc.contributor.authorJagersand, Martin
dc.date.accessioned2020-10-05
dc.date.accessioned2022-05-31T01:15:24Z
dc.date.available2022-05-31T01:15:24Z
dc.date.issued2017
dc.description.abstractThis paper proposes a novel image segmentation approach that integrates fully convolutional networks (FCNs) with a level set model. Compared with a FCN, the integrated method can incorporate smoothing and prior information to achieve an accurate segmentation. Furthermore, different than using the level set model as a post-processing tool, we integrate it into the training phase to fine-tune the FCN. This allows the use of unlabeled data during training in a semi-supervised setting. Using two types of medical imaging data (liver CT and left ventricle MRI data), we show that the integrated method achieves good performance even when little training data is available, outperforming the FCN or the level set model alone.
dc.description.urihttps://library.macewan.ca/cgi-bin/SFX/url.pl/BON
dc.identifier.citationTang M., Valipour S., Zhang Z., Cobzas D., Jagersand M. A Deep Level Set Method for Image Segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. DLMIA 2017, ML-CDS 2017. Lecture Notes in Computer Science, vol 10553. Springer https://link.springer.com/chapter/10.1007/978-3-319-67558-9_15
dc.identifier.urihttps://hdl.handle.net/20.500.14078/1762
dc.languageEnglish
dc.language.isoen
dc.rightsAll Rights Reserved
dc.subjectmedical imaging
dc.subjectCT
dc.subjectMRI
dc.subjectfully convolutional networks
dc.subjectFCNs
dc.subjectimage segmentation
dc.titleA deep level set method for image segmentationen
dc.typePresentation
dspace.entity.type

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