  We developed a new and computationally simple local block-wise self attention
based normal structures segmentation approach applied to head and neck computed
tomography (CT) images. Our method uses the insight that normal organs exhibit
regularity in their spatial location and inter-relation within images, which
can be leveraged to simplify the computations required to aggregate feature
information. We accomplish this by using local self attention blocks that pass
information between each other to derive the attention map. We show that adding
additional attention layers increases the contextual field and captures focused
attention from relevant structures. We developed our approach using U-net and
compared it against multiple state-of-the-art self attention methods. All
models were trained on 48 internal headneck CT scans and tested on 48 CT scans
from the external public domain database of computational anatomy dataset. Our
method achieved the highest Dice similarity coefficient segmentation accuracy
of 0.85$\pm$0.04, 0.86$\pm$0.04 for left and right parotid glands,
0.79$\pm$0.07 and 0.77$\pm$0.05 for left and right submandibular glands,
0.93$\pm$0.01 for mandible and 0.88$\pm$0.02 for the brain stem with the lowest
increase of 66.7\% computing time per image and 0.15\% increase in model
parameters compared with standard U-net. The best state-of-the-art method
called point-wise spatial attention, achieved \textcolor{black}{comparable
accuracy but with 516.7\% increase in computing time and 8.14\% increase in
parameters compared with standard U-net.} Finally, we performed ablation tests
and studied the impact of attention block size, overlap of the attention
blocks, additional attention layers, and attention block placement on
segmentation performance.
