Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships.
In many computerized methods for cell detection, segmentation, and classification in digital histopathology that have recently emerged, the task of cell segmentation remains a chief problem for image processing in designing computer-aided diagnosis (CAD) systems. In research and diagnostic studies o...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 55; no. 10; pp. 1829 - 1849 |
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| Autores principales: | , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=125206512&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 125206512 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Oct2017 vid: 55 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 125206512 125206512 144049899 NLM28247185 125206512 10.1007/s11517-017-1630-1 NLM28247185 125206512 ppf: 1829 ppct: 20 formats: fmt: @attributes: type: P tig: atl: Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships. aug: au: Hatipoglu, Nuh Bilgin, Gokhan sug: subj: Cell Separation Methods Image Processing, Computer Assisted Methods Neural Networks (Computer) Algorithms Funding Source Human ab: In many computerized methods for cell detection, segmentation, and classification in digital histopathology that have recently emerged, the task of cell segmentation remains a chief problem for image processing in designing computer-aided diagnosis (CAD) systems. In research and diagnostic studies on cancer, pathologists can use CAD systems as second readers to analyze high-resolution histopathological images. Since cell detection and segmentation are critical for cancer grade assessments, cellular and extracellular structures should primarily be extracted from histopathological images. In response, we sought to identify a useful cell segmentation approach with histopathological images that uses not only prominent deep learning algorithms (i.e., convolutional neural networks, stacked autoencoders, and deep belief networks), but also spatial relationships, information of which is critical for achieving better cell segmentation results. To that end, we collected cellular and extracellular samples from histopathological images by windowing in small patches with various sizes. In experiments, the segmentation accuracies of the methods used improved as the window sizes increased due to the addition of local spatial and contextual information. Once we compared the effects of training sample size and influence of window size, results revealed that the deep learning algorithms, especially convolutional neural networks and partly stacked autoencoders, performed better than conventional methods in cell segmentation. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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