Nasopharyngeal carcinoma (NPC) is a malignant tumor, and early diagnosis and timely treatment are important for NPC patients. Accurate and reliable detection of NPC lesions in magnetic resonance (MR) images is very helpful for the disease diagnosis. However, recent deep learning methods need to be improved for NPC detection in MR images. Because NPC tumors are invasive and usually small in size, it is difficult to distinguish NPC tumors from the closely connected surrounding tissues in a huge and complex background. In this paper, we propose an automatic detection method, named MWSR-YLCA, to accurately detect NPC lesions in MR images. Specifically, we design two modules, the multi-window settings resampling (MWSR) module and an improved YOLOv7 embedded with a coordinate attention mechanism (YLCA) module, to detect NPC lesions more accurately. First, the MWSR generates a pseudo-color version of MR images based on a multi-window resampling method, which preserves richer information. Subsequently, the YLCA detects the NPC lesion areas more accurately by constructing a novel network based on an improved YOLOv7 framework embedded with the coordinate attention mechanism. The proposed method was validated on an MR image set of 800 NPC patients and obtained 80.1% mAP detection performance with only 4694 data samples. The experimental results show that the proposed MWSR-YLCA method can perform high-accuracy detection of NPC lesions and has superior performance.

Fast and accurate lesion localization is an important step in medical image analysis. The current supervised deep learning methods have obvious limitations in the application of radiology, as they require a large number of manually annotated images. In response to the above issues, we introduced a deep reinforcement learning (DRL)-based method to locate nasopharyngeal carcinoma lesions in 3D-CT scans. The proposed method uses prior knowledge to guide the agent to reasonably reduce the search space and promote the convergence rate of the model. Furthermore, the multi-scale processing technique is also used to promote the localization of small objects. We trained the proposed model with 3D-CT scans of 50 patients and evaluated it with 3D-CT scans of 30 patients. The experimental results showed that the proposed model has strong robustness, and its accuracy was improved by more than 1 mm on average under the premise of using a smaller dataset compared with the DQN models in recent studies. The proposed model could effectively locate the lesion area of nasopharyngeal carcinoma in 3D-CT scans.