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dc.contributor.authorZhao, Amy (Xiaoyu Amy)
dc.contributor.authorBalakrishnan, Guha
dc.contributor.authorDurand, Fredo
dc.contributor.authorGuttag, John V
dc.contributor.authorDalca, Adrian Vasile
dc.date.accessioned2021-02-23T20:37:04Z
dc.date.available2021-02-23T20:37:04Z
dc.date.issued2019-06
dc.date.submitted2019-04
dc.identifier.isbn9781728132938
dc.identifier.issn1063-6919
dc.identifier.urihttps://hdl.handle.net/1721.1/129978
dc.description.abstractImage segmentation is an important task in many medical applications. Methods based on convolutional neural networks attain state-of-the-art accuracy; however, they typically rely on supervised training with large labeled datasets. Labeling medical images requires significant expertise and time, and typical hand-tuned approaches for data augmentation fail to capture the complex variations in such images. We present an automated data augmentation method for synthesizing labeled medical images. We demonstrate our method on the task of segmenting magnetic resonance imaging (MRI) brain scans. Our method requires only a single segmented scan, and leverages other unlabeled scans in a semi-supervised approach. We learn a model of transformations from the images, and use the model along with the labeled example to synthesize additional labeled examples. Each transformation is comprised of a spatial deformation field and an intensity change, enabling the synthesis of complex effects such as variations in anatomy and image acquisition procedures. We show that training a supervised segmenter with these new examples provides significant improvements over state-of-the-art methods for one-shot biomedical image segmentation.en_US
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionof10.1109/CVPR.2019.00874en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleData Augmentation Using Learned Transformations for One-Shot Medical Image Segmentationen_US
dc.typeArticleen_US
dc.identifier.citationZhao, Amy et al. “Data Augmentation Using Learned Transformations for One-Shot Medical Image Segmentation.” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019, Long Beach, California, Institute of Electrical and Electronics Engineers (IEEE), June 2019. © 2019 The Author(s)en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.relation.journal2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)en_US
dc.eprint.versionOriginal manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2020-12-11T17:15:44Z
dspace.orderedauthorsZhao, A; Balakrishnan, G; Durand, F; Guttag, JV; Dalca, AVen_US
dspace.date.submission2020-12-11T17:15:48Z
mit.journal.volume2019-Juneen_US
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusComplete


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