Anomaly detection in medical images is important in computer-aided diagnosis. It is a challenging task due to limited anomaly data, sample imbalance, and local differences between the normal and abnormal patterns. Abnormal manifestations in medical images have a definite clinical definition and descriptions, which can be introduced to improve the accuracy of detection rate. In this paper, we propose an anomaly detection method via image transformation surrogate tasks and apply it to detect the absence of bone wall in jugular bulb of temporal bone CT images. First, we design a pair of contrastive surrogate tasks, including an abnormal region completion and a normal background erasure, to decouple the similarity of the normal and abnormal examples. Then, image synthesis strategies for the surrogate tasks are designed, which alleviates the problem of limited abnormal data. Further, an abnormal scoring module is proposed, which includes MSE, SSIM, and local error intensity, to fuse the results of the surrogate tasks. We verify the effectiveness of our proposed method on the jugular bulb data set and experimental results show that the accuracy of our method is 0.995 and the AUC (Area Under the Curve) is 0.994.