Improving lung nodule segmentation in thoracic CT scans through the ensemble of 3D U-Net models.

导出 LinkOut

翻译打印收藏纠错

作者： Himanshu,Rikhari ^[1] ; Esha,Baidya Kayal ^[1] ; Shuvadeep,Ganguly ^[2] ; Archana,Sasi ^[2] ; Swetambri,Sharma ^[2] ; Ajith,Antony ^[3] ; Krithika,Rangarajan ^[4] ; Sameer,Bakhshi ^[2] ; Devasenathipathy,Kandasamy ^[3] ; Amit,Mehndiratta ^[5]

作者单位： Centre for Biomedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India. ^[1] Medical Oncology, Dr. B.R.A. IRCH, All India Institute of Medical Sciences New Delhi, New Delhi, India. ^[2] All India Institute of Medical Sciences New Delhi, New Delhi, India. ^[3] Dr. B.R.A. IRCH, All India Institute of Medical Sciences New Delhi, New Delhi, India. ^[4] Centre for Biomedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India. amit.mehndiratta@keble.oxon.org.;Department of Biomedical Engineering, All India Institute of Medical Sciences New Delhi, New Delhi, India. amit.mehndiratta@keble.oxon.org. ^[5]

关键词 3D U-Net Computed tomography Computer-aided diagnosis (CAD)Deep learning Lung nodule detection

主题词人类(Humans);体层摄影术, X线计算机(Tomography, X-Ray Computed);肺肿瘤(Lung Neoplasms);回顾性研究(Retrospective Studies);成像, 三维(Imaging, Three-Dimensional);孤立性肺结节(Solitary Pulmonary Nodule);放射摄影影像解释, 计算机辅助(Radiographic Image Interpretation, Computer-Assisted)

DOI 10.1007/s11548-024-03222-y

PMID 39044036

发布时间 2024-10-01

翻译满意度评价：

准确一般字义偏差语意偏离译文作用小提交