Soft Tissue Tracking during Brain Surgeries
Most of the tissue handled by the surgeon (or by a robotic hand) during brain surgeries is flexible: With the obvious exception of bone, which
Neurology
Next Generation Intra-op Navigation
During minimally invasive surgeries, surgeons are limited in their field of view, which restricts their observation of the patient’s full anatomy. This may lead to
AI-Assisted Brain Surgery
Brain surgeries are very complex: they need to be extremely accurate, since you want to spare healthy tissue; planning is very thorough, as the surgeon
Brain Ventricles Segmentation with Deep Learning
Early diagnosis and treatment of ventricular system pathologies is crucial. Brain CT has become a leading diagnostic tool due to its high availability and quick image generation, which is useful in emergency room settings such as stroke or traumatic brain injury (TBI). Backed by cutting edge deep neural network and advanced Artificial Intelligence techniques, CT imaging can perform a very accurate brain ventricles segmentation and supply the physicians with crucial information regarding presence of hemorrhage, ischemia, tumors, hydrocephalus, and other pathologies.
Read More
Brain Hemorrhage Segmentation with Deep Learning
Prompt diagnosis, monitoring and treatment of intracranial hemorrhage are essential to avoid brain structure damage. This task is made possible by recent AI-based advancements. Image analysis algorithms based on deep learning can rapidly estimate the hemorrhage volume and measure the edematous area around it. Automated image processing algorithms produce a 3D model of the ventricular system, which can ultimately be useful in guidance of the neurosurgeon during brain procedures.
Read More
Deep Learning in Brain Imaging
Recent years’ AI-based advancements in brain imaging have been outstanding. Many of them are precious for the physician to avoid or reduce structural damage and save lives. This article resumes some of those breakthrough innovations in brain imaging brought by Artificial intelligence, computer vision, deep learning and image analysis in performing crucial tasks of automated segmentation, registration, classification, image enhancement and more.
Read More
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,
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.
Read More
Brain tumor segmentation
In addition to primary tumors, the human brain can also suffer from secondary tumors or brain metastases. The most common cancers that spread from remote areas to the brain are lung, breast, melanoma, kidney, nasal cavity and colon cancers. By the way of segmenting the tumor in the image, brain tumor image processing overcomes anatomical structure challenges. AI-based techniques enable to estimate the volume and spread of the tumor and provide objective and variation-free expected tumor boundaries.
Read More
Soft Tissue Tracking during Brain Surgeries
Most of the tissue handled by the surgeon (or by a robotic hand) during brain surgeries is flexible: With the obvious exception of bone, which
Next Generation Intra-op Navigation
During minimally invasive surgeries, surgeons are limited in their field of view, which restricts their observation of the patient’s full anatomy. This may lead to
AI-Assisted Brain Surgery
Brain surgeries are very complex: they need to be extremely accurate, since you want to spare healthy tissue; planning is very thorough, as the surgeon
Brain Ventricles Segmentation with Deep Learning
Early diagnosis and treatment of ventricular system pathologies is crucial. Brain CT has become a leading diagnostic tool due to its high availability and quick image generation, which is useful in emergency room settings such as stroke or traumatic brain injury (TBI). Backed by cutting edge deep neural network and advanced Artificial Intelligence techniques, CT imaging can perform a very accurate brain ventricles segmentation and supply the physicians with crucial information regarding presence of hemorrhage, ischemia, tumors, hydrocephalus, and other pathologies.
Read More
Brain Hemorrhage Segmentation with Deep Learning
Prompt diagnosis, monitoring and treatment of intracranial hemorrhage are essential to avoid brain structure damage. This task is made possible by recent AI-based advancements. Image analysis algorithms based on deep learning can rapidly estimate the hemorrhage volume and measure the edematous area around it. Automated image processing algorithms produce a 3D model of the ventricular system, which can ultimately be useful in guidance of the neurosurgeon during brain procedures.
Read More
Deep Learning in Brain Imaging
Recent years’ AI-based advancements in brain imaging have been outstanding. Many of them are precious for the physician to avoid or reduce structural damage and save lives. This article resumes some of those breakthrough innovations in brain imaging brought by Artificial intelligence, computer vision, deep learning and image analysis in performing crucial tasks of automated segmentation, registration, classification, image enhancement and more.
Read More
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,
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.
Read More
Brain tumor segmentation
In addition to primary tumors, the human brain can also suffer from secondary tumors or brain metastases. The most common cancers that spread from remote areas to the brain are lung, breast, melanoma, kidney, nasal cavity and colon cancers. By the way of segmenting the tumor in the image, brain tumor image processing overcomes anatomical structure challenges. AI-based techniques enable to estimate the volume and spread of the tumor and provide objective and variation-free expected tumor boundaries.
Read More
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