  Gross tumor volume (GTV) delineation on tomography medical imaging is crucial
for radiotherapy planning and cancer diagnosis. Convolutional neural networks
(CNNs) has been predominated on automatic 3D medical segmentation tasks,
including contouring the radiotherapy target given 3D CT volume. While CNNs may
provide feasible outcome, in clinical scenario, double-check and prediction
refinement by experts is still necessary because of CNNs' inconsistent
performance on unexpected patient cases. To provide experts an efficient way to
modify the CNN predictions without retrain the model, we propose 3D-fused
context propagation, which propagates any edited slice to the whole 3D volume.
By considering the high-level feature maps, the radiation oncologists would
only required to edit few slices to guide the correction and refine the whole
prediction volume. Specifically, we leverage the backpropagation for activation
technique to convey the user editing information backwardly to the latent space
and generate new prediction based on the updated and original feature. During
the interaction, our proposed approach reuses the extant extracted features and
does not alter the existing 3D CNN model architectures, avoiding the
perturbation on other predictions. The proposed method is evaluated on two
published radiotherapy target contouring datasets of nasopharyngeal and
esophageal cancer. The experimental results demonstrate that our proposed
method is able to further effectively improve the existing segmentation
prediction from different model architectures given oncologists' interactive
inputs.
