Patient-specific and global convolutional neural networks for robust automatic liver tumor delineation in follow-up CT studies

Keyphrases

• Automatic Delineation 11%
• Baseline Scan 33%
• Computed Tomography 11%
• Convolutional Neural Network 100%
• CT Studies 100%
• Deep Learning Methods 11%
• Disease Assessment 11%
• Failure Correction 11%
• Failure Detection 11%
• Follow-up CT Scan 100%
• Limited Training Data 11%
• Liver Tumor 100%
• Longitudinal Follow-up 11%
• Medical Imaging 11%
• Neural Network Method 11%
• Neural Network Performance 11%
• Neural Network Segmentation 11%
• Patient Global 100%
• Patient-specific 100%
• Radiologists 11%
• Response Evaluation Criteria in Solid Tumors (RECIST) 11%
• Small Databases 11%
• Surface Distance 11%
• Tracking-by-detection 11%
• Training Data 11%
• Tumor 22%
• Tumor Characterization 11%
• Tumor Delineation 100%
• Tumor Measurement 11%
• Tumor Therapy 11%
• Tumor Tracking 11%
• User-dependent 11%
• Volumetric Measurement 11%

Computer Science

• Convolutional Neural Network 100%
• Deep Learning Method 10%
• Experimental Result 10%
• Failure Detection 10%
• Learning Approach 10%
• Medical Imaging 10%
• Network Performance 10%
• Network Segmentation 10%
• Size Measurement 10%
• Training Dataset 20%

Immunology and Microbiology

• Tumor Volume 50%
• X-Ray Computed Tomography 100%