  The nature of thick-slice scanning causes severe inter-slice discontinuities
of 3D medical images, and the vanilla 2D/3D convolutional neural networks
(CNNs) fail to represent sparse inter-slice information and dense intra-slice
information in a balanced way, leading to severe underfitting to inter-slice
features (for vanilla 2D CNNs) and overfitting to noise from long-range slices
(for vanilla 3D CNNs). In this work, a novel mesh network (MNet) is proposed to
balance the spatial representation inter axes via learning. 1) Our MNet
latently fuses plenty of representation processes by embedding
multi-dimensional convolutions deeply into basic modules, making the selections
of representation processes flexible, thus balancing representation for sparse
inter-slice information and dense intra-slice information adaptively. 2) Our
MNet latently fuses multi-dimensional features inside each basic module,
simultaneously taking the advantages of 2D (high segmentation accuracy of the
easily recognized regions in 2D view) and 3D (high smoothness of 3D organ
contour) representations, thus obtaining more accurate modeling for target
regions. Comprehensive experiments are performed on four public datasets
(CT\&MR), the results consistently demonstrate the proposed MNet outperforms
the other methods. The code and datasets are available at:
https://github.com/zfdong-code/MNet
