FourierNet: shape-preserving network for henle's fiber layer segmentation in optical coherence tomography images

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buir.contributor.authorCansız, Selahattin
buir.contributor.authorGündüz-Demir, Çiğdem
buir.contributor.orcidCansız, Selahattin|0000-0003-2991-6250
buir.contributor.orcidGündüz-Demir, Çiğdem|0000-0003-0724-1942
dc.citation.epage1047en_US
dc.citation.issueNumber2
dc.citation.spage1036
dc.citation.volumeNumber27
dc.contributor.authorCansız, Selahattin
dc.contributor.authorKesim, C.
dc.contributor.authorBektaş, S.N.
dc.contributor.authorKulalı, Z.
dc.contributor.authorHasanreisoğlu, M.
dc.contributor.authorGündüz-Demir, Çiğdem
dc.date.accessioned2024-03-15T12:01:45Z
dc.date.available2024-03-15T12:01:45Z
dc.date.issued2023-02-06
dc.departmentDepartment of Computer Engineering
dc.description.abstractHenle's fiber layer (HFL), a retinal layer located in the outer retina between the outer nuclear and outer plexiform layers (ONL and OPL, respectively), is composed of uniformly linear photoreceptor axons and Müller cell processes. However, in the standard optical coherence tomography (OCT) imaging, this layer is usually included in the ONL since it is difficult to perceive HFL contours on OCT images. Due to its variable reflectivity under an imaging beam, delineating the HFL contours necessitates directional OCT, which requires additional imaging. This paper addresses this issue by introducing a shape-preserving network, FourierNet, which achieves HFL segmentation in standard OCT scans with the target performance obtained when directional OCT is available. FourierNet is a new cascaded network design that puts forward the idea of benefiting the shape prior of the HFL in the network training. This design proposes to represent the shape prior by extracting Fourier descriptors on the HFL contours and defining an additional regression task of learning these descriptors. FourierNet then formulates HFL segmentation as concurrent learning of regression and classification tasks, in which estimated Fourier descriptors are used together with the input image to construct the HFL segmentation map. Our experiments on 1470 images of 30 OCT scans of healthy-looking macula reveal that quantifying the HFL shape with Fourier descriptors and concurrently learning them with the main segmentation task leads to significantly better results. These findings indicate the effectiveness of designing a shape-preserving network to facilitate HFL segmentation without performing directional OCT imaging.
dc.description.provenanceMade available in DSpace on 2024-03-15T12:01:45Z (GMT). No. of bitstreams: 1 FourierNet_shape-preserving_network_for_henle's_fiber_layer_segmentation_in_optical_coherence_tomography_images.pdf: 4717194 bytes, checksum: 3729c1d53d38dbb339e9e30c9b29eb98 (MD5) Previous issue date: 2023-02-01en
dc.identifier.doi10.1109/JBHI.2022.3225425
dc.identifier.eissn2168-2208
dc.identifier.issn2168-2194
dc.identifier.urihttps://hdl.handle.net/11693/114804
dc.language.isoEnglish
dc.publisherInstitute of Electrical and Electronics Engineers
dc.relation.isversionofhttps://dx.doi.org/10.1109/JBHI.2022.3225425
dc.source.titleIEEE Journal of Biomedical and Health Informatics
dc.subjectCascaded neural networks
dc.subjectFourier descriptors
dc.subjectFully convolutional networks
dc.subjectHenle's fiber layer segmentation
dc.subjectOptical coherence tomography
dc.subjectShape-preserving network
dc.titleFourierNet: shape-preserving network for henle's fiber layer segmentation in optical coherence tomography images
dc.typeArticle

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