BEM-RCNN Segmentation Based on the Inadequately Labeled Moving Mesenchymal Stem Cells

Li, Jingxiong; Wang, Yaqi; Zhang, Qianni

doi:10.1007/978-3-030-27272-2_34

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…The segmentation of label-free images can be particularly challenging ( Cameron et al, 2020 ; Liu et al, 2021a ), and as such a number of methods have been developed specifically for this task ( Vicar et al, 2019 ). A variety of segmentation methods designed for specific cell types ( Li J et al, 2019 ; Salvi et al, 2019 ) or datatypes, such as 3D images ( Çiçek et al, 2016 ), are also available. The performance of segmentation methods have been reviewed and compared in detail elsewhere ( Dima et al, 2011 ; Thomas and John, 2017 ; Caicedo et al, 2019 ; Cameron et al, 2020 ).…”

Section: Methods For Single-cell Imaging Data Analysismentioning

confidence: 99%

Computational Methods for Single-Cell Imaging and Omics Data Integration

Watson

Fard

Mar

2022

Front. Mol. Biosci.

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Integrating single cell omics and single cell imaging allows for a more effective characterisation of the underlying mechanisms that drive a phenotype at the tissue level, creating a comprehensive profile at the cellular level. Although the use of imaging data is well established in biomedical research, its primary application has been to observe phenotypes at the tissue or organ level, often using medical imaging techniques such as MRI, CT, and PET. These imaging technologies complement omics-based data in biomedical research because they are helpful for identifying associations between genotype and phenotype, along with functional changes occurring at the tissue level. Single cell imaging can act as an intermediary between these levels. Meanwhile new technologies continue to arrive that can be used to interrogate the genome of single cells and its related omics datasets. As these two areas, single cell imaging and single cell omics, each advance independently with the development of novel techniques, the opportunity to integrate these data types becomes more and more attractive. This review outlines some of the technologies and methods currently available for generating, processing, and analysing single-cell omics- and imaging data, and how they could be integrated to further our understanding of complex biological phenomena like ageing. We include an emphasis on machine learning algorithms because of their ability to identify complex patterns in large multidimensional data.

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