A Two-Tier Convolutional Neural Network for Combined Detection and Segmentation in Biological Imagery

dc.contributor.authorZiabari, Amirkoushyar
dc.contributor.authorShirinifard, Abbas
dc.contributor.authorEicholtz, Matthew R.
dc.contributor.authorSolecki, David J.
dc.contributor.authorRose, Derek C.
dc.date.accessioned2022-08-29T21:37:53Z
dc.date.available2022-08-29T21:37:53Z
dc.date.issued2019-11-14
dc.description.abstractDeep learning techniques have been useful for modern microscopy imaging techniques to further study and analyze biological structures and organs. Convolutional neural networks (CNN) have improved 2D object detection, localization, and segmentation. For imagery containing biological structures with depth, it is especially desirable to perform these tasks in 3D. Traditionally, performing these tasks simultaneously in 3D has proven to be computationally expensive. Currently available methodologies thus largely work to segment 3D objects from 2D images (without context from captured 3D volumes). In this work, we present a novel approach to perform fast and accurate localization, detection, and segmentation of volumes containing cells. Specifically, in our method, we modify and tune two state-of-the-art CNNs, namely 2D YOLOv2 and 3D U-Net, and combine them with a new fusion and image processing algorithms. Annotated volumes in this space are limited, and we have created synthetic data that mimics actual structures for training and testing our proposed approach. Promising results on this test data demonstrate the value of the technique and offers a methodology for 3D cell analysis in real microscopy imagery.en_US
dc.identifier.citationA. Ziabari, A. Shirinifard, M. R. Eicholtz, D. J. Solecki and D. C. Rose, "A Two-Tier Convolutional Neural Network for Combined Detection and Segmentation in Biological Imagery," 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2019, pp. 1-5, doi: 10.1109/GlobalSIP45357.2019.8969303.en_US
dc.identifier.doihttps://doi.org/10.1109/GlobalSIP45357.2019.8969303
dc.identifier.isbn978-1-7281-2723-1
dc.identifier.urihttp://hdl.handle.net/11416/695
dc.identifier.uri https://doi.org/10.1109/GlobalSIP45357.2019.8969303
dc.language.isoen_USen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.subjectMicroscopyen_US
dc.subjectConvolutional neural networksen_US
dc.titleA Two-Tier Convolutional Neural Network for Combined Detection and Segmentation in Biological Imageryen_US
dc.typeArticleen_US

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