GSM-Net: A global sequence modelling network for the segmentation of short axis CINE MRI images

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dc.contributor.authorUslu, Fatmatulzehra
dc.date.accessioned2026-02-12T21:04:51Z
dc.date.available2026-02-12T21:04:51Z
dc.date.issued2023
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractAtrial Fibrillation (AF) is a disease where the atria fail to properly contract but quiver instead, due to the abnormal electrical activity of the atrial tissue. In AF patients, anatomical and functional parameters of the left atrium (LA) largely differ from that of healthy people due to LA remodelling, which can continue in many cases after the catheter ablation treatment. Therefore, it is important to follow up with AF patients to detect any recurrence. LA segmentation masks obtained from short-axis CINE MRI images are used as the gold standard for the quantification of LA parameters. Thick slices of CINE MRI images hinder the use of 3D networks for segmentation while 2D architectures often fail to model inter-slice dependencies. This study presents GSM-Net which approximates 3D networks with effective modelling of inter-slice similarities with two new modules: global slice sequence encoder (GSSE) and sequence dependent channel attention module (SdCAt). In contrast to previous work modelling only local inter-slice similarities, GSSE also models global spatial dependencies across slices. SdCAt generates a distribution of attention weights over MRI slices per channel, to better trace characteristic changes in the size of the LA or other structures across slices. We found that GSM-Net outperforms previous methods on LA segmentation and helps to identify AF recurrence patients. We believe that GSM-Net can be used as an automatic tool to estimate LA parameters such as ejection fraction to identify AF, and to follow up with patients after treatment to detect any recurrence.
dc.description.sponsorshipBursa Technical University Scientific Research Projects Units in Turkiye [211N043]
dc.description.sponsorshipThis work is financially supported by Bursa Technical University Scientific Research Projects Units in Turkiye, with the project number of 211N043.
dc.identifier.doi10.1016/j.compmedimag.2023.102266
dc.identifier.issn0895-6111
dc.identifier.issn1879-0771
dc.identifier.pmid37385047
dc.identifier.scopus2-s2.0-85163975948
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.compmedimag.2023.102266
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6690
dc.identifier.volume108
dc.identifier.wosWOS:001033532000001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofComputerized Medical Imaging and Graphics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260212
dc.subjectCardiac image analysis
dc.subjectAtrial fibrillation
dc.subjectExplainability
dc.subjectChannel attention
dc.subjectSequence modelling
dc.subjectTransformers
dc.titleGSM-Net: A global sequence modelling network for the segmentation of short axis CINE MRI images
dc.typeArticle

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