A Computer-Aided Diagnosis System of Fetal Nucleated Red Blood Cells With Convolutional Neural Network.
* Context.--The rapid recognition of fetal nucleated red blood cells (fNRBCs) presents considerable challenges. Objective.--To establish a computer-aided diagnosis system for rapid recognition of fNRBCs by convolutional neural network. Design.--We adopted density gradient centrifugation and magnetic...
| Publicado en: | Archives of Pathology & Laboratory Medicine Vol. 146; no. 11; pp. 1395 - 1402 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
College of American Pathologists
Nov2022
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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=159889125&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159889125 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00039985 1FS jtl: Archives of Pathology & Laboratory Medicine issn: 00039985 maglogo: N pubinfo: dt: Nov2022 vid: 146 iid: 11 pid: 2550 pub: College of American Pathologists place: Northfield, Illinois artinfo: ui: 159889125 159889125 159889125 10.5858/arpa.2021-0142-OA 159889125 ppf: 1395 ppct: 7 formats: fmt: @attributes: type: P tig: atl: A Computer-Aided Diagnosis System of Fetal Nucleated Red Blood Cells With Convolutional Neural Network. aug: au: Chao Sun Ruijie Wang Lanbo Zhao Lu Han Sijia Ma Dongxin Liang Lei Wang Xiaoqian Tuo Yu Zhang Dexing Zhong Qiling Li affil: Department of Obstetrics and Gynecology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shannxi, China sug: subj: Erythrocytes Analysis Neural Networks (Computer) Diagnosis, Computer Assisted Human Centrifugation Fetal Blood Analysis Paraffin Embedding Staining and Labeling Automation Specimen Handling Methods Slides Sensitivity and Specificity ab: * Context.--The rapid recognition of fetal nucleated red blood cells (fNRBCs) presents considerable challenges. Objective.--To establish a computer-aided diagnosis system for rapid recognition of fNRBCs by convolutional neural network. Design.--We adopted density gradient centrifugation and magnetic-activated cell sorting to extract fNRBCs from umbilical cord blood samples. The cell-block method was used to embed fNRBCs for routine formalin-fixed paraffin sectioning and hematoxylin-eosin staining. Then, we proposed a convolutional neural network--based, computer- aided diagnosis system to automatically discriminate features and recognize fNRBCs. Extracting methods of interested region were used to automatically segment individual cells in cell slices. The discriminant information from cellular-level regions of interest was encoded into a feature vector. Pathologic diagnoses were also provided by the network. Results.--In total, 4760 pictures of fNRBCs from 260 cell-slides of 4 umbilical cord blood samples were collected. On the premise of 100% accuracy in the training set (3720 pictures), the sensitivity, specificity, and accuracy of cellular intelligent recognition were 96.5%, 100%, and 98.5%, respectively, in the test set (1040 pictures). Conclusions.--We established a computer-aided diagnosis system for effective and accurate fNRBC recognition based on a convolutional neural network. 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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