PERI-Net: a parameter efficient residual inception network for medical image segmentation

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dc.authorid0000-0001-7153-7583en_US
dc.contributor.authorUslu, Fatmatülzehra
dc.contributor.authorBass, Cher
dc.contributor.authorBharath, Anil A.
dc.date.accessioned2021-03-20T20:12:22Z
dc.date.available2021-03-20T20:12:22Z
dc.date.issued2020
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractRecent developments in deep networks allow us to train networks with more parameters by yielding better performance given sufficient amount of data. However, we are still restricted with the availability of labelled data in medical image segmentation, where the problem is exacerbated with high intra- and intervariability of anatomical structures. In order to bypass this problem without compromising network performance, this study introduces a PERI-Net, which promises to achieve higher performance while being with smaller parameter count such as on the order of 0.8 million than its counterparts. The network benefits from rich features generated by our versions of inception modules, better communication between encoding and decoding paths and an effective way of segmentation mask generation. We evaluate the performance of our architecture on the segmentation of retinal vasculature in fundus image datasets of DRIVE, CHASE_DB 1 and IOSTAR and the segmentation of axons in a 2-photon microscopy image dataset. According to the results of our experiments, PERI-Net achieves state of the art performance on sensitivity and G-mean metrics with a significant margin for the 3 datasets, by outperforming our training of a U-net sharing the same properties and training strategies as PERI-Net.en_US
dc.identifier.doi10.3906/elk-1912-97en_US
dc.identifier.endpage2277en_US
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.issue4en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage2261en_US
dc.identifier.urihttp://doi.org/10.3906/elk-1912-97
dc.identifier.urihttps://hdl.handle.net/20.500.12885/513
dc.identifier.volume28en_US
dc.identifier.wosWOS:000553764300003en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorUslu, Fatmatülzehra
dc.language.isoenen_US
dc.publisherTubitak Scientific & Technical Research Council Turkeyen_US
dc.relation.ispartofTurkish Journal Of Electrical Engineering And Computer Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectInception modulesen_US
dc.subjectU-neten_US
dc.subjectaxonen_US
dc.subjectresidual connectionsen_US
dc.subjectvesselsen_US
dc.titlePERI-Net: a parameter efficient residual inception network for medical image segmentationen_US
dc.typeArticleen_US

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