  In 3D medical image segmentation, small targets segmentation is crucial for
diagnosis but still faces challenges. In this paper, we propose the Axis
Projection Attention UNet, named APAUNet, for 3D medical image segmentation,
especially for small targets. Considering the large proportion of the
background in the 3D feature space, we introduce a projection strategy to
project the 3D features into three orthogonal 2D planes to capture the
contextual attention from different views. In this way, we can filter out the
redundant feature information and mitigate the loss of critical information for
small lesions in 3D scans. Then we utilize a dimension hybridization strategy
to fuse the 3D features with attention from different axes and merge them by a
weighted summation to adaptively learn the importance of different
perspectives. Finally, in the APA Decoder, we concatenate both high and low
resolution features in the 2D projection process, thereby obtaining more
precise multi-scale information, which is vital for small lesion segmentation.
Quantitative and qualitative experimental results on two public datasets (BTCV
and MSD) demonstrate that our proposed APAUNet outperforms the other methods.
Concretely, our APAUNet achieves an average dice score of 87.84 on BTCV, 84.48
on MSD-Liver and 69.13 on MSD-Pancreas, and significantly surpass the previous
SOTA methods on small targets.
