Adrenal Volume Quantitative Visualization Tool by Multiple Parameters and an nnU-Net Deep Learning Automatic Segmentation Model.
Abnormalities in adrenal gland size may be associated with various diseases. Monitoring the volume of adrenal gland can provide a quantitative imaging indicator for such conditions as adrenal hyperplasia, adrenal adenoma, and adrenal cortical adenocarcinoma. However, current adrenal gland segmentati...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 1; pp. 47 - 60 |
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| Autores principales: | , , , , , , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2025
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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=184471461&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184471461 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Feb2025 vid: 38 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184471461 184471461 184471461 10.1007/s10278-024-01158-y 184471461 ppf: 47 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Adrenal Volume Quantitative Visualization Tool by Multiple Parameters and an nnU-Net Deep Learning Automatic Segmentation Model. aug: au: Li, Yi Zhao, Yingnan Yang, Ping Li, Caihong Liu, Liu Zhao, Xiaofang Tang, Huali Mao, Yun affil: https://ror.org/033vnzz93 Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 400016, Chongqing, China sug: subj: Adrenal Glands Physiology Deep Learning Image Processing, Computer Assisted Diagnostic Imaging Automation Prediction Models Human Funding Source China Male Female Middle Age Aged Retrospective Panel Studies Academic Medical Centers Tomography, X-Ray Computed Data Analysis Software Comparative Studies Chi Square Test Fisher's Exact Test T-Tests Analysis of Variance Kruskal-Wallis Test Intraclass Correlation Coefficient Health Screening Monitoring, Physiologic Preoperative Care Imaging, Three-Dimensional Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Abnormalities in adrenal gland size may be associated with various diseases. Monitoring the volume of adrenal gland can provide a quantitative imaging indicator for such conditions as adrenal hyperplasia, adrenal adenoma, and adrenal cortical adenocarcinoma. However, current adrenal gland segmentation models have notable limitations in sample selection and imaging parameters, particularly the need for more training on low-dose imaging parameters, which limits the generalization ability of the models, restricting their widespread application in routine clinical practice. We developed a fully automated adrenal gland volume quantification and visualization tool based on the no new U-Net (nnU-Net) for the automatic segmentation of deep learning models to address these issues. We established this tool by using a large dataset with multiple parameters, machine types, radiation doses, slice thicknesses, scanning modes, phases, and adrenal gland morphologies to achieve high accuracy and broad adaptability. The tool can meet clinical needs such as screening, monitoring, and preoperative visualization assistance for adrenal gland diseases. Experimental results demonstrate that our model achieves an overall dice coefficient of 0.88 on all images and 0.87 on low-dose CT scans. Compared to other deep learning models and nnU-Net model tools, our model exhibits higher accuracy and broader adaptability in adrenal gland segmentation. 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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