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Search for temporal cell segmentation robustness in phase-contrast microscopy videos

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dc.affiliation.dptoUC3M. Departamento de Bioingenieríaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: BSEL - Laboratorio de Ciencia e Ingeniería Biomédicaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Biomedical Imaging and Instrumentation Groupes
dc.contributor.authorGómez de Mariscal, Estíbaliz
dc.contributor.authorJayatilaka, Hasini
dc.contributor.authorÇiçek, Özgün
dc.contributor.authorBrox, Thomas
dc.contributor.authorWirtz, Denis
dc.contributor.authorMuñoz Barrutia, María Arrate
dc.contributor.funderMinisterio de Ciencia e Innovación (España)es
dc.date.accessioned2023-05-31T11:38:41Z
dc.date.available2023-05-31T11:38:41Z
dc.date.issued2022
dc.descriptionProceeding of: Medical Imaging with Deep Learning (MIDL 2022), Zürich, Switzerland, 6-8 July 2022en
dc.description.abstractThis work presents a deep learning-based workflow to segment cancer cells embedded in D collagen matrices and imaged with phase-contrast microscopy under low magnification and strong background noise conditions. Due to the experimental and imaging setup, cell and protrusion appearance change largely from frame to frame. We use transfer learning and recurrent convolutional long-short term memory units to exploit the temporal information and provide temporally stable results. Our results show that the proposed approach is robust to weight initialization and training data sampling.en
dc.description.sponsorshipThis work was co-financed by ERDF, "A way of making Europe" (AMB), partially funded under Grant PID2019-109820RB-I00, MCIN/AEI/10.13039/501100011033/; the US NIH under Grants UO1AG060903 (DW) and U54CA143868 (DW). We acknowledge NVIDIA Corporation for the donation of the Titan X (Pascal) GPU.en
dc.format.extent3es
dc.identifier.bibliographicCitationGómez de Mariscal, Estibaliz, et al. Search for temporal cell segmentation robustness in phase-contrast microscopy videos. In: Medical Imaging with Deep Learning (MIDL 2022), Zürich, Switzerland, 6-8 July 2022en
dc.identifier.publicationfirstpage1es
dc.identifier.publicationlastpage3es
dc.identifier.urihttps://hdl.handle.net/10016/37399
dc.identifier.uxxiCC/0000034281
dc.language.isoengen
dc.relation.eventdate2022-07-06es
dc.relation.eventplaceZürich, SUIZAes
dc.relation.eventtitleMedical Imaging with Deep Learning (MIDL 2022)en
dc.relation.projectIDGobierno de España. PID2019-109820RB-I00es
dc.relation.publisherversionhttps://openreview.net/forum?id=QzZE_PJi49uen
dc.rights© 2022 E. Gómez-de-Mariscal, H. Jayatilaka, . Çiçek, T. Brox, D.W. & A. Muñoz-Barrutia.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaBiología y Biomedicinaes
dc.subject.otherCell segmentationen
dc.subject.otherTransfer-learningen
dc.subject.otherConvLSTMen
dc.subject.otherPhase-contrast microscopyen
dc.titleSearch for temporal cell segmentation robustness in phase-contrast microscopy videosen
dc.typeconference proceedings*
dspace.entity.typePublication
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