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nnU-Net
Paper_nnU-Net.pptx
Paper_nnU-Net.pptx
Paper_nnU-Net.pptx
固定パラメータ
1Block =
Conv+InstanceNorm+LeakyReLU
Paper_nnU-Net.pptx
Paper_nnU-Net.pptx
Paper_nnU-Net.pptx
データ特性
特徴分布
平均形状
モダリティ
濃度
データ特性による構成の決定
後処理
固定パラメータ
データ特性
固定パラメータ
データ特性による
構成の決定
後処理
最適なモデルの選択
モデルの生成例1
ACDC
モデルの生成例2
LiTS
Paper_nnU-Net.pptx
Paper_nnU-Net.pptx
Paper_nnU-Net.pptx
入力形式 Nifti
• 入力形式はNifti(拡張子.nii)でなくてはならない
• DICOM 商用
• Nifti 研究用
• 変換
• DICOM からは簡単
• 画像 からは面倒
• モデルは簡略化した形式で可視化できる
1. 畳み込みのカーネルサイズ
2. ダウンサンプリングのストライド
3. ブロックの数
Paper_nnU-Net.pptx
まとめ
• nnUNetでは,アーキテクチャではなく
パイプラインからのアプローチを行った
• 様々な医用画像のタスクで上位の性能を誇る
• 1つの指標として非常に有用な手法である

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Editor's Notes

  1. ・ 3D U-Net cascade のトリガー 3Dフル解像度U-Netのパッチサイズが リサンプリング画像の中央値の12.5%未満 ・ 3D U-Net 低解像度 パッチサイズ、ネットワークトポロジー、バッチサイズ(上述)を再構成しながら、 設定したパッチサイズが中央値画像形状の25%をカバーするまで、ターゲット間隔を反復的に増加させる。
  2. 性能