AI for Neurology and Brain

The treatment of numerous pathologies originated in the brain is going through a revolution caused by Artificial Intelligence. In all the complexity and sophistication of the brain and regardless of the many secrets it still keeps for scientists, more and more studies are helping in the detection of brain diseases as well in their classification and the choice of a treatment: tumors, hemorrhages, traumas, strokes, exposures to radiations or chemicals, metastases, infections, genetic abnormalities and other severe conditions benefit form the progress in AI technology. Image processing of the brain, powered by deep learning techniques, is able today to give answers still unthinkable not long ago. See below some of RSIP Vision's research and projects in the area of computer vision for neurology and brain healthcare.

Brain Lesion Detection in MRI

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...

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...

Brain Ventricles Segmentation with AI

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 pathologiesRead more...

Deep Learning in Brain Imaging and Brain Microscopy

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...