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Table 3 Comparison with the state-of-the-art methods in KiTS19.

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

Authors

methods

Kidney Dice

Tumor Dice

This paper

2.5D MFFAU-Net

0.975

0.872

Fabian et al. [20].

3D U-Net

0.974

0.851

Kang et al. [21].

3D-CNN + ConvLSTM

0.964

0.789

da Cruz et al. [22].

AlexNet + 2D U-Net

0.930

\

Pandey et al. [23].

active contour + deep learning

0.976

\

da Cruz et al. [28].

2.5D DeepLabv3+

\

0.852

Causey et al. [29].

Ensemble of U-Net

0.949

0.601

Kittipongdaja et al. [30].

2.5D DenseU-Net

0.960

\

Türk et al. [31].

Hybrid V-Net

0.977

0.865

Türk et al. [32].

Two-Stage Bottleneck Block

\

0.869