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Table 4 Comparison with the state-of-the-art methods in KiTS21.

From: 2.5D MFFAU-Net: a convolutional neural network for kidney segmentation

Authors

methods

Kidney Dice/SD

Mass Dice/SD

Tumor Dice/SD

Cyst Dice/SD

This paper

2.5D MFFAU-Net

0.973/0.941

0.887/0.788

0.873/0.769

0.765/0.678

Shen et al. [24].

COTRNet

0.923/0.885

0.553/0.369

0.506/0.355

\

Adam et al. [25].

3D U-ResNet

0.951/0.904

0.798/0.648

0.781/0.627

\

Zhao et al. [26].

nnU-Net

0.975/0.949

0.885/0.787

0.869/0.769

\

Zang et al. [33].

3D U-Net

0.970/0.937

0.851/0.720

0.819/0.700

\

Chen et al. [34].

ResSENormUnet + DenseTransUnet

0.943/ \

\

0.778/ \

0.710/ \

Li et al. [35].

3D deep learning

0.960/ \

\

0.815/ \

0.450/ \

George [36].

3D U-Net

0.975/0.945

\

0.871/0.761

0.881/0.773