関連する実験動画

Updated: Feb 16, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

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2つのディープラーニングモデルの説明性に基づいたベンチマーク,MR誘導型適応放射線療法におけるリスクのある臓器のセグメンテーションのための2つのディープラーニングモデル

H Sekkat1, A Khallouqi2, Y Hammouga3

  • 1Laboratory of Sciences and Engineering of Biomedicals, Biophysics and Health, Higher Institute of Health Sciences, Hassan 1st University, Settat, Morocco; Higher Institute of Nursing Professions and Health Techniques, Rabat, Morocco.

Journal of medical imaging and radiation sciences
|February 14, 2026
PubMed
まとめ

この研究では,U-NetとResidual U-Netのディープラーニングモデルの両方が,MRIスキャンでリスクのある胃腸臓器のセグメント化に比較可能で信頼性の高いパフォーマンスを提供することが判明しました. それらの説明性と安定性は,MR誘導型適応放射線療法における潜在的な使用を支えています.

キーワード:
MR誘導放射線療法 (MRgRT) とはMRIセグメンテーション MRIセグメンテーション危険にさらされている臓器 (OAR)Resunetはリニューアルしました.U-net は,U-net を利用している.

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