BMAI
Biomedical Artificial
Intelligence Research Unit
Biomedical Artificial
Intelligence Research Unit
En
We developed an automated scheme for segmenting and calculating liver volume in hepatic CT by means of 3D fast-marching and level-set segmentation algorithms. Automatic liver segmentation on hepatic CT images is challenging because the liver often abuts other organs of similar density. Our purpose was to develop an automated liver segmentation scheme based on a 3D level-set algorithm for measur Read more...
Enhanced Digital Chest Radiography: Temporal Subtraction Combined with Virtual Dual-EnergyETechnology for Improved Conspicuity of Growing Cancers and Other Pathologic Changes
We developed a novel temporal-subtraction (TS) technique combined with virtual dual-energyEtechnology for improved conspicuity of growing cancers and other pathologic changes in digital chest radiography (CXR). Digi Read more...
– A CAD Utilizing 3D Massive-Training ANNs for Detection of Flat Lesions in CT Colonography in a Large Multicenter Clinical Trial
– Polyp Detection in CT Colonography: Performance of a CAD Scheme Incorporating 3D MTANNs on False-Negative Polyps in a Multicenter Clinical Trial
– Ensemble Training for a Mixture of Expert 3D MTANNs for Eliminating Multiple False-Positive Source Read more...
– Automated CT Liver Volumetry by Use of Three-Dimensional Fast-Marching and Level-Set Segmentation
– Reduction of Quantum Noise in Low-Dose Double-Contrast Radiographs of the Stomach
– Enhanced Digital Chest Radiography: Temporal Subtraction Combined with Virtual Dual-EnergyETechnology for Improved Conspicuity of Growing Cancers and Other Pathologic Changes
– Virtu Read more...
– A CAD Utilizing 3D Massive-Training ANNs for Detection of Flat Lesions in CT Colonography in a Large Multicenter Clinical Trial
– Polyp Detection in CT Colonography: Performance of a CAD Scheme Incorporating 3D MTANNs on False-Negative Polyps in a Multicenter Clinical Trial
– Ensemble Training for a Mixture of Expert 3D MTANNs for Eliminating Multiple False-Positive Source Read more...
– Automated CT Liver Volumetry by Use of Three-Dimensional Fast-Marching and Level-Set Segmentation
– Reduction of Quantum Noise in Low-Dose Double-Contrast Radiographs of the Stomach
– Enhanced Digital Chest Radiography: Temporal Subtraction Combined with Virtual Dual-EnergyETechnology for Improved Conspicuity of Growing Cancers and Other Pathologic Changes
– Virtu Read more...
– A CAD Utilizing 3D Massive-Training ANNs for Detection of Flat Lesions in CT Colonography in a Large Multicenter Clinical Trial
– Polyp Detection in CT Colonography: Performance of a CAD Scheme Incorporating 3D MTANNs on False-Negative Polyps in a Multicenter Clinical Trial
– Ensemble Training for a Mixture of Expert 3D MTANNs for Eliminating Multiple False-Positive Source Read more...
– Neural Edge Enhancer for Supervised Edge Enhancement from Noisy Images
– Analysis of the Neural Edge Enhancer Trained for Edge Enhancement in Noisy Images
– Reduction of Noise from Images by Use of a Neural Filter
– A Method for Designing the Optimal Structure of a Neural Filter
– Efficient Approximation of Neural Filters for Removing Read more...
– Automated CT Liver Volumetry by Use of Three-Dimensional Fast-Marching and Level-Set Segmentation
– A CAD Utilizing 3D Massive-Training ANNs for Detection of Flat Lesions in CT Colonography in a Large Multicenter Clinical Trial
– Polyp Detection in CT Colonography: Performance of a CAD Scheme Incorporating 3D MTANNs on False-Negative Polyps in a Multicenter Clinical Trial< Read more...