Bone Metastatic Tumor Detection based on AnoGAN Using CT Images

Abstract

In this paper, we propose a method to detect bone metastatic tumors using computed tomography (CT) images. Bone metastatic tumors spread from primary cancer to other organs, and they can cause severe pain. Therefore, it is important to detect metastatic tumors earlier in addition to primary cancer. However, since metastatic tumors are very small, and they emerge from unpredictable regions in the body, collecting metastatic tumor images is difficult compared to primary cancer. In such a case, it can be considered that the idea of anomaly detection is suitable. The proposed method based on a generative adversarial network model trains with only non-metastatic bone tumor images and detects bone metastatic tumor in an unsupervised manner. Then the anomaly score is defined for each test CT image. Experimental results show the anomaly scores between non-metastatic bone tumor images and metastatic bone tumor images are clearly different. The anomaly detection approach may be effective for the detection of bone metastatic tumors in CT images.