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.

翻译：在3D医学图像分割中,小目标部分对诊断至关重要,但仍面临挑战。在本文件中,我们提议3D医学图像分割采用名为APAUNet的轴射射引号UNet,以进行3D医学图像分割,特别是小目标。考虑到3D特征空间背景的很大比例,我们引入了一个预测战略,将3D特征投射成三个正方位的2D平面,以从不同观点中获取背景关注。这样,我们可以过滤冗余特征信息,并减轻3D扫描中小损害的关键信息的损失。然后,我们利用一个维度混合战略,将3D特征与不同轴的注意力结合起来,并用加权加法将其合并,以适应性地学习不同观点的重要性。最后,在APA Decoder中,我们将高分辨率和低分辨率特征组合成三个方位的2D投影程序,从而获得更精确的多尺度信息,这对小型偏差部分分析至关重要。两个公共数据集(BTCV和MSD)的定量和定性实验结果显示,我们拟议的APA-48网络在以往的MSDMSD方法上大大超越了SDMSD的平均水平。