Liver tumor detection based on objects as points.

The automatic detection of liver tumors by computed tomography is challenging, owing to their wide variations in size and location, as well as to their irregular shapes. Existing detection methods largely rely on two-stage detectors and use CT images marked with bounding boxes for training and detection. In this study, we propose a single-stage detector method designed to accurately detect multiple tumors simultaneously, and provide results demonstrating its increased speed and efficiency compared to prior methods. The proposed model divides CT images into multiple channels to obtain continuity information and implements a bounding box attention mechanism to overcome the limitation of inaccurate prediction of tumor center points and decrease redundant bounding boxes. The model integrates information from various channels using an effective Squeeze-and-Excitation attention module. The proposed model obtained a mean average precision result of 0.476 on the Decathlon dataset, which was superior to that of the prior methods examined for comparison. This research is expected to enable physicians to diagnose tumors very efficiently; particularly, the prediction of tumor center points is expected to enable physicians to rapidly verify their diagnostic judgments. The proposed method is considered suitable for future adoption in clinical practice in hospitals and resource-poor areas because its superior performance does not increase computational cost; hence, the equipment required is relatively inexpensive.