  A 3D deep learning model (OARnet) is developed and used to delineate 28 H&N
OARs on CT images. OARnet utilizes a densely connected network to detect the
OAR bounding-box, then delineates the OAR within the box. It reuses information
from any layer to subsequent layers and uses skip connections to combine
information from different dense block levels to progressively improve
delineation accuracy. Training uses up to 28 expert manual delineated (MD) OARs
from 165 CTs. Dice similarity coefficient (DSC) and the 95th percentile
Hausdorff distance (HD95) with respect to MD is assessed for 70 other CTs.
Mean, maximum, and root-mean-square dose differences with respect to MD are
assessed for 56 of the 70 CTs. OARnet is compared with UaNet, AnatomyNet, and
Multi-Atlas Segmentation (MAS). Wilcoxon signed-rank tests using 95% confidence
intervals are used to assess significance. Wilcoxon signed ranked tests show
that, compared with UaNet, OARnet improves (p<0.05) the DSC (23/28 OARs) and
HD95 (17/28). OARnet outperforms both AnatomyNet and MAS for DSC (28/28) and
HD95 (27/28). Compared with UaNet, OARnet improves median DSC up to 0.05 and
HD95 up to 1.5mm. Compared with AnatomyNet and MAS, OARnet improves median
(DSC, HD95) by up to (0.08, 2.7mm) and (0.17, 6.3mm). Dosimetrically, OARnet
outperforms UaNet (Dmax 7/28; Dmean 10/28), AnatomyNet (Dmax 21/28; Dmean
24/28), and MAS (Dmax 22/28; Dmean 21/28). The DenseNet architecture is
optimized using a hybrid approach that performs OAR-specific bounding box
detection followed by feature recognition. Compared with other auto-delineation
methods, OARnet is better than or equal to UaNet for all but one geometric
(Temporal Lobe L, HD95) and one dosimetric (Eye L, mean dose) endpoint for the
28 H&N OARs, and is better than or equal to both AnatomyNet and MAS for all
OARs.
