This paper investigates the problem of circular structure detection, which is of great significance in medical detection scenarios. Existing detection methods usually treat this problem as a special case of object detection and adapt existing object detectors. However, these methods ignore the inherent properties of graphs, leading to poor model-checking performance and weak generalization ability. Compared with complex shape targets, the geometric properties of circles are simpler, so the graphics parameters can be found in the complex parameter space through specific feature extractors for shape detection. This paper presents an end-to-end deep learning framework for circular object detection in medical images. Leveraging deep features extracted by convolutional neural networks (CNNs), the proposed method integrates a dynamically differentiable Hough transform module to effectively map image-domain features to the parameter space of circles, defined by their center coordinates and radii. This parameter space's aggregation and projection mechanism concentrates features onto potential circular trajectories, generating distinct peak responses. The proposed approach achieves an efficient and fully differentiable detection pipeline by transforming object detection into a peak localization task, eliminating the need for traditional non-maximum suppression (NMS). In addition, this paper also uses the circular IoU loss function for circular objects. The experiment shows that the dynamic deep circular Hough transform and circular IoU loss function have better detection effects on medical circular objects.