Deep learning-based lower limb deep vein thrombosis detection method, medium and terminal

Abstract

The invention discloses a Deep learning-based lower limb deep vein thrombosis detection method, a medium and a terminal, and the detection method comprises the steps of obtaining a sample of a magnetic resonance imaging image of a detection object to form a training set, and framing a thrombosis detection frame in the magnetic resonance imaging image in the training set; constructing a thrombus detection network model, and inputting the training set into the thrombus detection network model for training to obtain a trained thrombus detection network model, wherein the thrombus detection network model is constructed based on a YOLOv3 detection network, and a five-channel image matrix is used as input of the thrombus detection network model; and inputting a to-be-detected magnetic resonanceimaging image into the trained thrombus detection network model to obtain a vein thrombus detection result. High detection efficiency is achieved in a medical image with a complex background, and a doctor is assisted in rapidly and accurately completing diagnosis of the lower limb DVT.

Description

technical field [0001] The invention relates to the field of medical imaging technology, in particular to a deep learning-based deep vein of lower extremity [0002] The detection method, medium and terminal of thrombus. Background technique [0003] Deep venous thrombosis (DVT) is a venous return disorder caused by abnormal coagulation of blood in deep veins. The annual incidence is about 0.1%, and it is increasing year by year. It has become the third major cardiovascular disease. In addition to symptoms such as lower limb swelling and pain, more than 50% of DVT patients are prone to concurrent pulmonary embolism, and the mortality rate exceeds 20%. [0004] There are many imaging methods currently used for DVT examination, including ultrasound, computed tomography (CT), magnetic resonance imaging (Magnetic Resonance Imaging, MRI), and digital subtraction techniques. As a non-invasive examination, MRI has the advantages of good soft tissue contrast, full field of view, a...

AI Technical Summary

Problems solved by technology

However, the difference in experience between different doctors can easily lead to uneven diagnostic results. At the same time, the parameters of the MRI scan image are complex, the size of the thrombus is different, and the position is different. Doctors need to spend a lot of time and energy in the process of reading the film.

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