  Although deep convolutional networks have been widely studied for head and
neck (HN) organs at risk (OAR) segmentation, their use for routine clinical
treatment planning is limited by a lack of robustness to imaging artifacts, low
soft tissue contrast on CT, and the presence of abnormal anatomy. In order to
address these challenges, we developed a computationally efficient nested block
self-attention (NBSA) method that can be combined with any convolutional
network. Our method achieves computational efficiency by performing non-local
calculations within memory blocks of fixed spatial extent. Contextual
dependencies are captured by passing information in a raster scan order between
blocks, as well as through a second attention layer that causes bi-directional
attention flow. We implemented our approach on three different networks to
demonstrate feasibility. Following training using 200 cases, we performed
comprehensive evaluations using conventional and clinical metrics on a separate
set of 172 test scans sourced from external and internal institution datasets
without any exclusion criteria. NBSA required a similar number of computations
(15.7 gflops) as the most efficient criss-cross attention (CCA) method and
generated significantly more accurate segmentations for brain stem (Dice of
0.89 vs. 0.86) and parotid glands (0.86 vs. 0.84) than CCA. NBSA's
segmentations were less variable than multiple 3D methods, including for small
organs with low soft-tissue contrast such as the submandibular glands (surface
Dice of 0.90).
