Spleen Tissue Segmentation Algorithm for Cryo-Imaging Data.
Spleen tissue segmentation is an essential process for analyzing various immunological diseases as observed in the cryo-imaging data. Because manual labeling of the spleen tissue by human experts is not efficient, an automatic segmentation algorithm is needed. In this study, we developed a novel alg...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 2; pp. 588 - 603 |
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| Autores principales: | , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2023
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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=162679412&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 162679412 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2023 vid: 36 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 162679412 160460141 162679412 162679412 10.1007/s10278-022-00736-2 162679412 ppf: 588 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Spleen Tissue Segmentation Algorithm for Cryo-Imaging Data. aug: au: Wuttisarnwattana, Patiwet Auephanwiriyakul, Sansanee affil: Department of Computer Engineering, Faculty of Engineering, Chiang Mai University, 50300, Chiang Mai, Thailand sug: subj: Spleen Algorithms Diagnostic Imaging Biological Phenomena Animal Studies Mice Funding Source Sensitivity and Specificity Deep Learning Immunologic Diseases Graft Versus Host Disease ab: Spleen tissue segmentation is an essential process for analyzing various immunological diseases as observed in the cryo-imaging data. Because manual labeling of the spleen tissue by human experts is not efficient, an automatic segmentation algorithm is needed. In this study, we developed a novel algorithm for automatically segmenting spleen substructures including white pulp and red pulp for the first time. The algorithm is designed for datasets created by a cryo-imaging system. This unique technology can effectively enable cellular tracking anywhere in the whole mouse with single-cell sensitivity. The proposed algorithm consists of four components: initial spleen mask creation, feature extraction, Supervised Patch-based Fuzzy c-Mean (spFCM) classification, and post-processing. The algorithm accurately and efficiently labeled spleen tissues in all experiment settings. The algorithm also improved the spleen segmentation throughput by 90 folds as compared to the manual segmentation. Moreover, we show that our novel spFCM algorithm outperformed traditional fast-learning classifiers as well as the U-Net deep-learning model in many aspects. Two major contributions of this paper are (1) an explainable algorithm for segmenting spleen tissues in cryo-images for the first time and (2) an spFCM algorithm as a new classifier. We also discussed that our work can be beneficial to researchers who work not only in the fields of graft-versus-host disease (GVHD) mouse models, but also in that of other immunological disease models where spleen analysis is essential. Future work building upon our research may lay the foundations for biomedical studies that utilize cryo-imaging technology. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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