APAUNet: Axis Projection Attention UNet for Small Target in 3D Medical Segmentation

TL;DR

APAUNet introduces a novel axis projection attention mechanism in a UNet architecture to improve small target segmentation in 3D medical images, effectively capturing multi-view contextual information.

Contribution

The paper proposes APAUNet, a new 3D segmentation model that uses axis projection attention and multi-scale fusion to enhance small lesion detection in medical scans.

Findings

Achieves higher dice scores on BTCV and MSD datasets.

Outperforms state-of-the-art methods in small target segmentation.

Effectively captures multi-view contextual information.

Abstract

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.