Feasibility study of unsupervised anomaly detection using Wasserstein GAN in SPECT image.

purposeIn this study, we constructed an anomaly detection system based on the Wasserstein generative model in brain single-photon emission computed tomography (SPECT) images, and conducted a basic study on the feasibility of the system.

methodsThe proposed method used a Wasserstein generative adversarial network approach based on optimal transport theory. Anomaly detection was performed using only healthy images, and based on the anomaly score calculated from the loss function. Theoretical validation was performed using a numerical phantom which simulated medical image with varying image noise and signal levels. The results were evaluated with receiver operating characteristic curves and area under the curve (AUC). Brain SPECT images from clinical scenarios were used to investigate the feasibility of this system through subtraction images and various quantitative evaluations.

resultsThe numerical phantom showed the relationship between the noise and signal values of the system, and the anomaly detection ability based on the anomaly score value indicated an AUC of 0.9994. The correlation between the anomaly score and the anomaly image was confirmed in the brain SPECT image, and the detection region was clearly observed in the difference image.

conclusionWe proposed and investigated the feasibility of an anomaly detection system based on the Wasserstein generative model. Its effectiveness is expected to reduce the workload of human operators.