  Recently, 3D medical image reconstruction (MIR) and segmentation (MIS) based
on deep neural networks have been developed with promising results, and
attention mechanism has been further designed to capture global contextual
information for performance enhancement. However, the large size of 3D volume
images poses a great computational challenge to traditional attention methods.
In this paper, we propose a folded attention (FA) approach to improve the
computational efficiency of traditional attention methods on 3D medical images.
The main idea is that we apply tensor folding and unfolding operations with
four permutations to build four small sub-affinity matrices to approximate the
original affinity matrix. Through four consecutive sub-attention modules of FA,
each element in the feature tensor can aggregate spatial-channel information
from all other elements. Compared to traditional attention methods, with
moderate improvement of accuracy, FA can substantially reduce the computational
complexity and GPU memory consumption. We demonstrate the superiority of our
method on two challenging tasks for 3D MIR and MIS, which are quantitative
susceptibility mapping and multiple sclerosis lesion segmentation.
