DeepDistance: a multi-task deep regression model for cell detection in inverted microscopy images

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buir.contributor.authorKoyuncu, Can Fahrettin
buir.contributor.authorGüneşli, Gözde Nur
buir.contributor.authorGündüz-Demir, Çigdem
buir.contributor.authorÇetin-Atalay, Rengül
dc.citation.epage101720-11en_US
dc.citation.spage101720-1en_US
dc.citation.volumeNumber63en_US
dc.contributor.authorKoyuncu, Can Fahrettinen_US
dc.contributor.authorGüneşli, Gözde Nuren_US
dc.contributor.authorÇetin-Atalay, Rengülen_US
dc.contributor.authorGündüz-Demir, Çigdemen_US
dc.date.accessioned2021-03-05T05:04:33Z
dc.date.available2021-03-05T05:04:33Z
dc.date.issued2020
dc.departmentDepartment of Computer Engineeringen_US
dc.departmentInterdisciplinary Program in Neuroscience (NEUROSCIENCE)en_US
dc.description.abstractThis paper presents a new deep regression model, which we call DeepDistance, for cell detection in images acquired with inverted microscopy. This model considers cell detection as a task of finding most probable locations that suggest cell centers in an image. It represents this main task with a regression task of learning an inner distance metric. However, different than the previously reported regression based methods, the DeepDistance model proposes to approach its learning as a multi-task regression problem where multiple tasks are learned by using shared feature representations. To this end, it defines a secondary metric, normalized outer distance, to represent a different aspect of the problem and proposes to define its learning as complementary to the main cell detection task. In order to learn these two complementary tasks more effectively, the DeepDistance model designs a fully convolutional network (FCN) with a shared encoder path and end-to-end trains this FCN to concurrently learn the tasks in parallel. For further performance improvement on the main task, this paper also presents an extended version of the DeepDistance model that includes an auxiliary classification task and learns it in parallel to the two regression tasks by also sharing feature representations with them. DeepDistance uses the inner distances estimated by these FCNs in a detection algorithm to locate individual cells in a given image. In addition to this detection algorithm, this paper also suggests a cell segmentation algorithm that employs the estimated maps to find cell boundaries. Our experiments on three different human cell lines reveal that the proposed multi-task learning models, the DeepDistance model and its extended version, successfully identify the locations of cell as well as delineate their boundaries, even for the cell line that was not used in training, and improve the results of its counterparts.en_US
dc.description.provenanceSubmitted by Zeynep Aykut (zeynepay@bilkent.edu.tr) on 2021-03-05T05:04:33Z No. of bitstreams: 1 DeepDistance_a_multi_task_deep_regression_model_for_cell.pdf: 2515476 bytes, checksum: e812c4af18c56f346085c29641e556a3 (MD5)en
dc.description.provenanceMade available in DSpace on 2021-03-05T05:04:33Z (GMT). No. of bitstreams: 1 DeepDistance_a_multi_task_deep_regression_model_for_cell.pdf: 2515476 bytes, checksum: e812c4af18c56f346085c29641e556a3 (MD5) Previous issue date: 2020en
dc.embargo.release2022-07-01
dc.identifier.doi10.1016/j.media.2020.101720en_US
dc.identifier.issn1361-8415
dc.identifier.urihttp://hdl.handle.net/11693/75796
dc.language.isoEnglishen_US
dc.publisherElsevieren_US
dc.relation.isversionofhttps://dx.doi.org/10.1016/j.media.2020.101720en_US
dc.source.titleMedical Image Analysisen_US
dc.subjectMulti-task learningen_US
dc.subjectFeature learningen_US
dc.subjectFully convolutional networken_US
dc.subjectCell detectionen_US
dc.subjectCell segmentationen_US
dc.subjectInverted microscopy image analysisen_US
dc.titleDeepDistance: a multi-task deep regression model for cell detection in inverted microscopy imagesen_US
dc.typeArticleen_US

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