MRA-TOFからの自動セグメンテーションを使用して,イテラティブの擬似ラベル付けを使用して,破裂していない頭蓋内動脈瘤の意思決定を改善する
Anouk S Verschuur1, Jiaxin Zhang2, Maarten J Kamphuis2
1From the Image Sciences Institute (A.S.V., J.Z., M.J.K., C.M.W.T., I.C.V.D.S.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands and CUBRIC, School of Physics and Astronomy (C.M.W.T.), Cardiff University, CF10 3AT, Cardiff, United Kingdom. a.s.verschuur@alumnus.utwente.nl.
AJNR. American journal of neuroradiology
|February 14, 2026
まとめ
ディープラーニングモデルであるDIVA-segは,MRA-TOFスキャンから頭蓋内血管と動脈瘤を正確に分割します. この自動化されたアプローチは,3D形態学的評価を改善し,観察者の変動性を減らすことにより,臨床使用の可能性を示しています.
科学分野:
- メディカルイマージング (医学イメージング)
- 医療における人工知能
- 神経画像は,神経イメージングによるものです.
背景:
- 正確な頭蓋内動脈瘤の評価は,治療計画とリスクの階層化に不可欠です.
- 手動セグメンテーションは時間がかかり,オブザーバー間およびオブザーバー内での変動が起こりやすい.
- 既存の自動化された方法は,しばしば正確さ,強度,または十分な検証が欠けている.
研究 の 目的:
- MRA-TOFを用いた頭蓋内血管と動脈瘤のセグメンテーションのためのディープラーニングベースの方法であるDIVA-segの開発と検証.
- 正確な3D形態学的評価を可能にし,臨床意思決定を支援します.
- 信頼性の高い,一般化可能な自動化された動脈瘤セグメンテーションツールに対する満たされていないニーズに対処するために.
主な方法:
- 3つのMRA-TOFデータセットを使用しました:ラベル付き (トレーニング/テスト),ラベルなし (擬似ラベル付け) および外部検証.
- ラベルが付けられていないデータに対して,擬似ラベルを生成するための,イテラティブ nnU-Net アプローチを使用しました.
- 偽ラベルとオリジナルのラベルデータを組み込んだ2番目のnnU-Netモデルをトレーニングし,DSC,ハウズドルフ距離,3D形態学的尺度を使用してパフォーマンスを評価しました.
主要な成果:
- DIVA-segは,血管 (0.925) と動脈瘤 (0.880) に対して,高い内部ダイス類似系数 (DSC) を達成しました.
- 外部検証では,血管のDSCは0.899,動脈瘤のDSCは0.861で,高いパフォーマンスを示した.
- 平均ハウズドルフ距離は0.7mm未満であり,3D形態学的測定では高い一致が観察され,特定のメトリックでは小さな比例バイアスが観察されました.
結論:
- DIVA-segは,頭蓋内血管と動脈瘤の正確で信頼できるセグメンテーションを提供します.
- このモデルは,内部および外部データセットの間で一貫したパフォーマンスを実証しています.
- 3D形態学的測定に関する高い合意は,動脈瘤のモニタリングと意思決定の合理化のための重要な臨床的有用性を示唆しています.
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