Diagnosis method for bone metastasis tumor in nuclide bone imaging based on deep learning

Abstract

The invention provides a method for diagnosing bone metastases in nuclide bone imaging based on deep learning. The method relates to a bone scanning diagnosis classification model, a bone metastasis tumor region segmentation model and a bone metastasis tumor load evaluation and automatic report generation model. By the adoption of the method, the bone metastasis tumor can be judged, automatic region segmentation can be conducted, the recognition accuracy is high, and full-automatic analysis from original image input to report generation is preliminarily achieved.

Description

technical field [0001] The invention relates to the technical field of medical image processing, in particular to a method for diagnosing bone metastases in nuclide bone imaging based on deep learning. Background technique [0002] The incidence of bone metastases is 35 to 40 times that of primary bone tumors. Cancer bone metastasis is one of the main causes of cancer pain. It causes pathological fractures, spinal cord compression, hypercalcemia and bone marrow failure and other complications, which accelerate the development of the disease and seriously affect the quality of life of cancer patients. In the past ten years, many disciplines have made unremitting efforts in the pathogenesis, prevention and treatment of bone metastases, but so far no effective cure has been found. [0003] At present, bone scan is the most commonly used examination method for the analysis of bone metastases and the evaluation of curative effect. The radionuclide used 99m Tc-MDP is imaged by t...

AI Technical Summary

Problems solved by technology

[0004] At present, the analysis of whole-body bone scan images mainly relies on the manual reading of nuclear medicine physicians, and according to the physician's personal experience, the diagnosis is made by checking the whole-body bone scan images to find abnormal lesion locations. Physicians have to do a lot of repetitive work for this

Due to the high complexity of image acquisition, the great variability among patients, the poor image quality, and the large subjective factors in manual film reading, there are deviations in the analysis and diagnosis results of whole-body bone scan images, which are prone to misdiagnosis and missed diagnosis.

In addition, technicians can adjust the brightness and contrast of the original image before producing the final image. However, different technicians may set different parameters for the image display system, which will make doctors encounter difficulties when determining subtle bone lesions. to difficulty

At the same time, before producing the final image, technicians can adjust the brightness and contrast of the original image. However, different technicians may set different parameters for the image display system, which will make doctors in determining subtle bone lesions encountered difficulties

Images