Deep Learning-Assisted Diffusion Tensor Imaging for Evaluation of the Physis and Metaphysis.
Diffusion tensor imaging of physis and metaphysis can be used as a biomarker to predict height change in the pediatric population. Current application of this technique requires manual segmentation of the physis which is time-consuming and introduces interobserver variability. UNET Transformers (UNE...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 2; pp. 756 - 766 |
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| Autores principales: | , , , , , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2024
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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=177626026&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177626026 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2024 vid: 37 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 177626026 177626026 177626026 10.1007/s10278-024-00993-3 177626026 ppf: 756 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Learning-Assisted Diffusion Tensor Imaging for Evaluation of the Physis and Metaphysis. aug: au: Duong, Phuong T. Santos, Laura Hsu, Hao-Yun Jambawalikar, Sachin Mutasa, Simukayi Nguyen, Michael K. Guariento, Andressa Jaramillo, Diego affil: https://ror.org/01esghr10 Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA sug: subj: Body Height In Infancy and Childhood Femur Anatomy and Histology Femur Radiography Deep Learning Utilization Neural Networks (Computer) Image Processing, Computer Assisted Human Retrospective Design Prospective Studies Magnetic Resonance Imaging Child Male Female Intraclass Correlation Coefficient Child: 6-12 years Male Female ab: Diffusion tensor imaging of physis and metaphysis can be used as a biomarker to predict height change in the pediatric population. Current application of this technique requires manual segmentation of the physis which is time-consuming and introduces interobserver variability. UNET Transformers (UNETR) can be used for automatic segmentation to optimize workflow. Three hundred and eighty-five DTI scans from 191 subjects with mean age of 12.6 years ± 2.01 years were retrospectively used for training and validation. The mean Dice correlation coefficient was 0.81 for the UNETR model and 0.68 for the UNET. Manual extraction and segmentation took 15 min per volume, whereas both deep learning segmentation techniques took < 1 s per volume and were deterministic, always producing the same result for a given input. Intraclass correlation coefficient (ICC) for ROI-derived femur diffusion metrics was excellent for tract count (0.95), volume (0.95), and FA (0.97), and good for tract length (0.87). The results support the hypothesis that a hybrid UNETR model can be trained to replace the manual segmentation of physeal DTI images, therefore automating the process. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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