Dental Segmentation with Deep Learning
Dental problems affect people of all ages and ethnic groups, and are common worldwide. With many patients suffering from tooth decay, orthodontic issues and even
Tag: Graph cuts
Intracranial Hemorrhage and Edema Segmentation
An intracranial hemorrhage (ICH) is a condition in which a blood vessel erupts inside the brain, causing internal bleeding. If not treated correctly and immediately,
CT Segmentation in orthopedic surgery
CT image segmentation is a typical phase of orthopedic surgeries in which a visualization system is called to visually support the surgeon’s task. This system
Coronary CT Angiography with Deep Learning
The production of 3D CT images of the heart requires a fast image processing technology, applied simultaneously on multiple scanned layers. To automatically separate the different components of the image, our software locates in the images the muscular layer (myocardium) of the heart needed for the rest of the segmentation in coronary CT angiography. Minimum graph cuts is the technique which provides the clearest tracking and the strongest segmentation results.
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Retinal inner layers segmentation
OCT is the only method that can perform noninvasive imaging with non-ionizing radiation and offering relatively good resolution. That is why it has become a
Geographic Atrophy (GA)
Geographic Atrophy (generally called GA) is a case of advanced Dry AMD (Age-related Macular Degeneration) which might lead to vision loss. As a consequence of
Brain Lesion Detection in MRI Images
Individuals diagnosed with central nervous system (CNS) tumors often suffer from disabilities caused by dysfunctional neurological state and deterioration in systemic activity, leading to relative short expected life-span post diagnosis. Automated segmentation of irregular 3D shapes from MRI volumetric data assists oncologists in their prognosis of these lesions. AI-based methods based on deep learning methodologies, together with imaging techniques in brain lesion detection have been demonstrated in numerous applications to perform accurately and robustly to support the physician.
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Bone segmentation in orthopedic surgery
Incorporation of new visualization technologies and planning methodologies shortens examination, planning and operation procedures in orthopedic surgery, while retaining the high standard of accuracy that are required in these common practices. Innovative algorithmic techniques, relying on image processing, computer vision and machine learning are increasingly utilized and have gained approval by regulatory bodies such as the FDA, leading to what is better known as Computer Assisted Orthopedic Surgery (CAOS) procedures. This requires accurate bone segmentation, better performed using the method which we recommend.
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