  In this work, we propose an adversarial attack-based data augmentation method
to improve the deep-learning-based segmentation algorithm for the delineation
of Organs-At-Risk (OAR) in abdominal Computed Tomography (CT) to facilitate
radiation therapy. We introduce Adversarial Feature Attack for Medical Image
(AFA-MI) augmentation, which forces the segmentation network to learn
out-of-distribution statistics and improve generalization and robustness to
noises. AFA-MI augmentation consists of three steps: 1) generate adversarial
noises by Fast Gradient Sign Method (FGSM) on the intermediate features of the
segmentation network's encoder; 2) inject the generated adversarial noises into
the network, intentionally compromising performance; 3) optimize the network
with both clean and adversarial features. Experiments are conducted segmenting
the heart, left and right kidney, liver, left and right lung, spinal cord, and
stomach. We first evaluate the AFA-MI augmentation using nnUnet and TT-Vnet on
the test data from a public abdominal dataset and an institutional dataset. In
addition, we validate how AFA-MI affects the networks' robustness to the noisy
data by evaluating the networks with added Gaussian noises of varying
magnitudes to the institutional dataset. Network performance is quantitatively
evaluated using Dice Similarity Coefficient (DSC) for volume-based accuracy.
Also, Hausdorff Distance (HD) is applied for surface-based accuracy. On the
public dataset, nnUnet with AFA-MI achieves DSC = 0.85 and HD = 6.16
millimeters (mm); and TT-Vnet achieves DSC = 0.86 and HD = 5.62 mm. AFA-MI
augmentation further improves all contour accuracies up to 0.217 DSC score when
tested on images with Gaussian noises. AFA-MI augmentation is therefore
demonstrated to improve segmentation performance and robustness in CT
multi-organ segmentation.
