  Tomography medical imaging is essential in the clinical workflow of modern
cancer radiotherapy. Radiation oncologists identify cancerous tissues, applying
delineation on treatment regions throughout all image slices. This kind of task
is often formulated as a volumetric segmentation task by means of 3D
convolutional networks with considerable computational cost. Instead, inspired
by the treating methodology of considering meaningful information across
slices, we used Gated Graph Neural Network to frame this problem more
efficiently. More specifically, we propose convolutional recurrent Gated Graph
Propagator (GGP) to propagate high-level information through image slices, with
learnable adjacency weighted matrix. Furthermore, as physicians often
investigate a few specific slices to refine their decision, we model this
slice-wise interaction procedure to further improve our segmentation result.
This can be set by editing any slice effortlessly as updating predictions of
other slices using GGP. To evaluate our method, we collect an Esophageal Cancer
Radiotherapy Target Treatment Contouring dataset of 81 patients which includes
tomography images with radiotherapy target. On this dataset, our convolutional
graph network produces state-of-the-art results and outperforms the baselines.
With the addition of interactive setting, performance is improved even further.
Our method has the potential to be easily applied to diverse kinds of medical
tasks with volumetric images. Incorporating both the ability to make a feasible
prediction and to consider the human interactive input, the proposed method is
suitable for clinical scenarios.
