Abstract:
Deep learning-based automatic recognition of the scanning or exposing region in medical imaging automation is a promising new technique that can reduce radiographer workload, optimize imaging workflow, and improve image quality. X-ray imaging research and practice are scarce. This paper addresses two key X-ray imaging automation issues: automatic exposure moment and exposure region recognition. Thus, we propose a real-time hybrid model-based automatic video analysis framework. The framework has three interdependent parts: Body Structure Detection, Motion State Tracing, and Body Modeling. Body Structure Detection disassembles the patient to get body keypoints and Bboxes. Combining and analyzing the two types of body structure representations yields rich spatial location information about the patient body structure. Motion State Tracing analyzes the exposure region’s motion state to determine exposure time. Body Modeling calculates the exposure region when exposure occurs. The proposed method is tested on a large X-ray examination scene dataset. The proposed method automatically recognizes the exposure moment and region. This paradigm is the first to automatically and accurately recognize the exposure region in X-ray imaging without a radiographer.
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