Deep Learning for the Automatic Diagnosis and Analysis of Bone Metastasis on Bone Scintigrams

作者：Simin Liu, Ming Feng, Tingting Qiao, Haidong Cai, Kele Xu, Xiaqing Yu, Wen Jiang, Zhongwei Lv, Yin Wang, Dan Li · 发表于：DOAJ (DOAJ: Directory of Open Access Journals) · 年份：2022 · 被引用次数：37 · 研究领域：Medical Imaging and Pathology Studies、Radiomics and Machine Learning in Medical Imaging、Medical Imaging Techniques and Applications

Simin Liu,1,* Ming Feng,2,* Tingting Qiao,1,* Haidong Cai,1 Kele Xu,3 Xiaqing Yu,1 Wen Jiang,1 Zhongwei Lv,1 Yin Wang,2 Dan Li1 1Department of Nuclear Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, People’s Republic of China; 2School of Electronic and Information Engineering, Tongji University, Shanghai, People’s Republic of China; 3National Key Laboratory of Parallel and Distributed Processing, National University of Defense Technology, Changsha, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhongwei Lv; Dan LiDepartment of Nuclear Medicine, Shanghai Tenth People’s Hospital, Tongji University, Yanchangzhong Road 301, Shanghai, 200072, People’s Republic of ChinaTel +86 21-66302075Email Lvzwjs2020@163.com; plumredlinda@163.comObjective: To develop an approach for automatically analyzing bone metastases (BMs) on bone scintigrams based on deep learning technology.Methods: This research included a bone scan classification model, a regional segmentation model, an assessment model for tumor burden and a diagnostic report generation model. Two hundred eighty patients with BMs and 341 patients with non-BMs were involved. Eighty percent of cases were randomly extracted from two groups as training set. Remaining cases were as testing set. A deep residual convolutional neural network with different structures was used to determine whether me...