Automatic cell segmentation in histopathological images via two-staged superpixel-based algorithms.
The analysis of cell characteristics from high-resolution digital histopathological images is the standard clinical practice for the diagnosis and prognosis of cancer. Yet, it is a rather exhausting process for pathologists to examine the cellular structures manually in this way. Automating this ted...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 57; no. 3; pp. 653 - 666 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Mar2019
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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=135086831&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135086831 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Mar2019 vid: 57 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 135086831 135086831 NLM30327998 10.1007/s11517-018-1906-0 NLM30327998 135086831 ppf: 653 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automatic cell segmentation in histopathological images via two-staged superpixel-based algorithms. aug: au: Albayrak, Abdulkadir Bilgin, Gokhan affil: Department of Computer Engineering, Yildiz Technical University (YTU), 34220, Istanbul, Turkey sug: subj: Kidney Neoplasms Pathology Histocytological Preparation Techniques Methods Algorithms Image Processing, Computer Assisted Methods Carcinoma, Renal Cell Pathology Cluster Analysis Resource Databases Scales ab: The analysis of cell characteristics from high-resolution digital histopathological images is the standard clinical practice for the diagnosis and prognosis of cancer. Yet, it is a rather exhausting process for pathologists to examine the cellular structures manually in this way. Automating this tedious and time-consuming process is an emerging topic of the histopathological image-processing studies in the literature. This paper presents a two-stage segmentation method to obtain cellular structures in high-dimensional histopathological images of renal cell carcinoma. First, the image is segmented to superpixels with simple linear iterative clustering (SLIC) method. Then, the obtained superpixels are clustered by the state-of-the-art clustering-based segmentation algorithms to find similar superpixels that compose the cell nuclei. Furthermore, the comparison of the global clustering-based segmentation methods and local region-based superpixel segmentation algorithms are also compared. The results show that the use of the superpixel segmentation algorithm as a pre-segmentation method improves the performance of the cell segmentation as compared to the simple single clustering-based segmentation algorithm. The true positive ratio (TPR), true negative ratio (TNR), F-measure, precision, and overlap ratio (OR) measures are utilized as segmentation performance evaluation. The computation times of the algorithms are also evaluated and presented in the study. Graphical Abstract The visual flowchart of the proposed automatic cell segmentation in histopathological images via two-staged superpixel-based algorithms. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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