Comparative Performance Evaluation of Federated and Centralized Learning for Velum and OTE Segmentation in Sleep Endoscopy Images.
Accurate segmentation of upper airway structures such as the velum and OTE (oropharynx, tongue base, epiglottis) in drug-induced sleep endoscopy (DISE) images is crucial for predicting the degree and location of obstruction to determine treatment options for obstructive sleep apnea (OSA). This study...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 39; no. 4; pp. 3773 - 3784 |
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| Autores principales: | , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2026
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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=196241822&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 196241822 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: Aug2026 vid: 39 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 196241822 189902488 196241822 196241822 10.1007/s10278-025-01756-4 196241822 ppf: 3773 ppct: 11 formats: tig: atl: Comparative Performance Evaluation of Federated and Centralized Learning for Velum and OTE Segmentation in Sleep Endoscopy Images. aug: au: Yeom, Jong Chan Kim, Jin Youp Kim, Young Jae Kim, Kwang Gi Rhee, Cha-Seo affil: https://ror.org/005nteb15 Department of Bio-health Medical Engineering, Gachon University, Gil Medical Center, Incheon, Republic of Korea sug: subj: Federated Learning Endoscopy Image Processing, Computer Assisted Sleep Apnea, Obstructive Diagnosis Tongue Radiography Sensitivity and Specificity Human Comparative Studies Videorecording Funding Source Epiglottis Radiography Oropharynx Radiography Machine Learning Deep Learning Reproducibility of Results Radiographic Image Interpretation, Computer-Assisted Convolutional Neural Networks Predictive Validity Academic Medical Centers South Korea Oropharynx Anatomy and Histology Confidence Intervals Artifacts Airway Obstruction Radiography Sedation Retrospective Design Record Review T-Tests Descriptive Statistics Multicenter Studies ab: Accurate segmentation of upper airway structures such as the velum and OTE (oropharynx, tongue base, epiglottis) in drug-induced sleep endoscopy (DISE) images is crucial for predicting the degree and location of obstruction to determine treatment options for obstructive sleep apnea (OSA). This study systematically compares centralized learning (CL) and federated learning (FL) approaches for the semantic segmentation of these regions using multi-institutional DISE video data. A convolutional neural network (CNN)-based segmentation model was trained and evaluated for both learning paradigms. The results consistently showed that the CL approach achieved statistically significantly higher segmentation performance across all metrics—precision, recall, accuracy, and Dice similarity coefficient (DSC)—for both the velum and OTE regions compared with FL. For the velum region, CL achieved a DSC of 85.91 ± 1.01%, compared with FL's 81.78 ± 0.58%. Similarly, for the OTE region, CL achieved an average DSC of 87.04 ± 0.41%, whereas FL achieved 85.20 ± 0.25%. Further analysis revealed that while both models struggled with ambiguous boundaries and anatomical variability—particularly for the tongue base—the epiglottis and oropharynx lateral wall were segmented with higher accuracy. These findings underscore the need for advanced techniques in FL, such as improved optimization algorithms and methods to address data heterogeneity, to narrow the performance gap with CL. This study provides foundational insights for developing more robust and clinically applicable deep learning models for upper airway analysis, emphasizing the importance of future research into advanced FL strategies and real-world validation. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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