MTFD-Net: left atrium segmentation in CT images through fractal dimension estimation

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buir.contributor.authorSaber Jabdaragh, Aziza
buir.contributor.orcidSaber Jabdaragh, Aziza|0000-0002-4012-2294
dc.citation.epage114en_US
dc.citation.spage108
dc.citation.volumeNumber173
dc.contributor.authorSaber Jabdaragh, Aziza
dc.contributor.authorFirouznia, M.
dc.contributor.authorFaez, K.
dc.contributor.authorAlikhani, F.
dc.contributor.authorAlikhani Koupaei, J.
dc.contributor.authorGündüz-Demir, Ç.
dc.date.accessioned2024-03-13T08:14:20Z
dc.date.available2024-03-13T08:14:20Z
dc.date.issued2023-08-18
dc.departmentDepartment of Computer Engineering
dc.description.abstractMulti-task learning proved to be an effective strategy to increase the performance of a dense prediction network on a segmentation task, by defining auxiliary tasks to reflect different aspects of the problem and concurrently learning them with the main task of segmentation. Up to now, previous studies defined their auxiliary tasks in the Euclidean space. However, for some segmentation tasks, the complexity and high variation in the texture of a region of interest may not follow the smoothness constraint in the Euclidean geometry. This paper addresses this issue by introducing a new multi-task network, MTFD-Net, which utilizes the fractal geometry to quantify texture complexity through self-similar patterns in an image. To this end, we propose to transform an image into a map of fractal dimensions and define its learning as an auxiliary task, which will provide auxiliary supervision to the main segmentation task, towards betterment of left atrium (LA) segmentation in computed tomography (CT) images. To the best of our knowledge, this is the first proposal of a dense prediction network that employs the fractal geometry to define an auxiliary task and learns it in parallel to the segmentation task in a multi-task learning framework. Our experiments revealed that the proposed MTFD-Net model led to more accurate LA segmentations compared to its counterparts.
dc.identifier.doi10.1016/j.patrec.2023.08.005
dc.identifier.eissn1872-7344
dc.identifier.issn0167-8655
dc.identifier.urihttps://hdl.handle.net/11693/114664
dc.language.isoen
dc.publisherElsevier BV * North-Holland
dc.relation.isversionofhttps://dx.doi.org/10.1016/j.patrec.2023.08.005
dc.rightsCC BY 4.0 DEED (Attribution 4.0 International)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.source.titlePattern Recognition Letters
dc.subjectFractal dimension
dc.subjectMultitask learning
dc.subjectDense prediction networks
dc.subjectComputed tomography
dc.subjectSegmentation
dc.titleMTFD-Net: left atrium segmentation in CT images through fractal dimension estimation
dc.typeArticle

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