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

• Main Authors: LIU SIMIN, FENG MING, WANG YIN, LI DAN, LYU ZHONGWEI
• Format: Patent
• Language: Chinese; English
• Published: 09.04.2021
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; HANDLING RECORD CARRIERS; HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS; PHYSICS; PRESENTATION OF DATA; RECOGNITION OF DATA; RECORD CARRIERS

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. 本发明提供一种基于深度学习的核素骨显像中骨转移瘤的诊断方法。所述方法涉及骨扫描诊断分类模型、骨转移瘤区域分割模型、骨转移瘤负荷评估及自动报告生成模型。采用本发明的方法能够判别骨转移瘤并进行自动区域分割，识别准确率较高，初步实现了从原始图像输入到报告生成的全自动分析。