  Organs-at-risk (OAR) delineation in computed tomography (CT) is an important
step in Radiation Therapy (RT) planning. Recently, deep learning based methods
for OAR delineation have been proposed and applied in clinical practice for
separate regions of the human body (head and neck, thorax, and abdomen).
However, there are few researches regarding the end-to-end whole-body OARs
delineation because the existing datasets are mostly partially or incompletely
annotated for such task. In this paper, our proposed end-to-end convolutional
neural network model, called \textbf{AttentionAnatomy}, can be jointly trained
with three partially annotated datasets, segmenting OARs from whole body. Our
main contributions are: 1) an attention module implicitly guided by body region
label to modulate the segmentation branch output; 2) a prediction
re-calibration operation, exploiting prior information of the input images, to
handle partial-annotation(HPA) problem; 3) a new hybrid loss function combining
batch Dice loss and spatially balanced focal loss to alleviate the organ size
imbalance problem. Experimental results of our proposed framework presented
significant improvements in both S{\o}rensen-Dice coefficient (DSC) and 95\%
Hausdorff distance compared to the baseline model.
