Surgical Workflow Analysis
Surgical workflow analysis is an important safety guard for the surgeon: with it, a computer is able to scan a video of a surgery, either offline after it has already been performed or online during the surgery itself, and automatically identify at what stage the surgery is at. Endoscopy has revo...
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Challenges and AI Solutions for Endoscopy
We have learned about EndoVis at MICCAI 2019 and on a Computer Vision News medical imaging review with Stefanie Speidel. Led by Stefanie herself with Lena Maier-Hein and Danail Stoyanov, EndoVis has posted a vision CAI (Computer-Assisted Interventions) challenge to evaluate the current state of t...
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Multiplex IF Analysis
Multiplex IF analysis allows for real-time relational data between cells in tissue. This is an advantage over the gold standard brightfield IHC, which looks at a single marker at a time. When using brightfield IHC for multiple markers, sequential sections of the tissue are needed which does not a...
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Circulating Tumor Cells (CTCs)
Circulating tumor cells (CTCs) are rare cancer cells that originate from a tumor and then travel through the patient’s blood or lymphatic system. CTCs have a high risk of metastasizing elsewhere in the body, which can be very dangerous. It is well-known that finding and analyzing these rare cells...
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Coronary Arteries Segmentation
Coronary artery disease (CAD) or ischemic heart disease (IHD) has become one of the most common causes of morbidity and mortality worldwide. Patients who suffer from CAD, usually have accumulation of deposits such as lipids and inflammatory cells within the coronary arteries, potentially causing ...
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Great Vessels Segmentation with Deep Learning
The great vessels conduct blood to and from the heart. These vessels include the aorta, superior and inferior vena cava, pulmonary arteries and pulmonary veins. Since the great vessels are an integral part of systemic and pulmonary circulation, vascular pathologies involving said vessels might be...
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Middle and Inner Ear Segmentation with Deep Learning
Ear pathologies are common in all age groups, and are one of the leading causes for visiting a doctor. In most cases, proper diagnosis can be achieved via clinical examination. However, in recurrent or complicated cases imaging of the ear is indicated and can help with treatment choice. Th...
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Larynx Segmentation with Deep Learning
The larynx, also known as the voice box, is a triangular structure in charge of important functions including breathing, voice production and supplying protection to the trachea from aspirations. It consists mostly of cartilages- epiglottic, thyroid, cricoid, arytenoid, corniculate and cuneiform....
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Lymph Node Segmentation Module
Lymph nodes are routinely examined and assessed during physical examination of patients in a clinic or hospital setting. Enlarged lymph nodes can be indicators of infection, cancer and other pathologies. Therefore, a biopsy of a suspected lymph node, which provides tissue histology or cytology is...
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Brain Ventricles Segmentation with Deep Learning
The brain ventricular system is in charge of cerebrospinal fluid (CSF) production, which is essential for its normal function. In addition to providing physical protection to the brain tissue, it provides the nervous system with nutrients and removes waste among other functions. The ventricular s...
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Brain Hemorrhage Segmentation with Deep Learning
Intracranial hemorrhage is defined as bleeding within the cranium. Bleeding can occur within the brain parenchyma itself, giving way to what is called intra-axial bleed or intracerebral hemorrhage. On the other hand, bleeding outside the brain tissue is referred to as extra-axial bleed. Subtypes ...
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Detection and Tracking of Tumors
RSIP Vision’s automated detection and tracking of tumors answers one of the most frequent needs in the pharma industry – assessment of patient response to a new drug. This very strong tool saves precious time in the clinical stage when the automated detection and tracking can supply the most accu...
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Automated RECIST Measurement
RSIP Vision has develop an efficient tool for clinical trials in the pharma industry – the automated RECIST measurement. The gold standard in measuring the evolution of solid tumors, along the treatment with a tested drug, is the RECIST score (Response Evaluation Criteria in Solid Tumors), ...
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Intrusion Detection with Deep Learning
Detecting physical and virtual intrusions is a key process in ensuring information and property security. Physical intrusion detection refers to all attempts at break-ins to a building, warehouse, or other perimeters by an unauthorized person, where access is granted to only limited personnel. Ba...
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Chest CT Scan Analysis with Deep Learning
Chest radiography, with modalities such as X-Ray and CT, is now the common practice for the detection and analysis of the progression of lung tumors, tuberculosis and other pulmonary abnormalities. To date, most analysis are done by expert radiographers, who analyze resulting scans and estimate p...
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Object Tracking at High fps
Object tracking in video sequences is a classical challenge in computer vision, which finds applications in nearly all domains of the industry: from assembly line automation, security, traffic control, automatic driving assistance systems and agriculture. Presently state of the art algorithms per...
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High Resolution Image Reconstruction
Recovering a high-resolution (HR) image from a low resolution one is a classical problem in computer vision for which many algorithms have been developed to date. Most notably, methodologies using sparse coding: these techniques have achieved current state-of-the-art results, but suffer from long...
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Automated Defect Inspection Using Deep Learning
Defect detection during production is a necessary step to ensure product quality. Although manual human inspections are still being employed, automated visual inspection has practically replaced manual labor in almost all major production lines and is ubiquitous in mechanical parts manufacturing,...
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Deep Learning in Cardiology
1.1 Segmentation tasks [10] suggest a new fully convolutional network architecture for the task of cardiovascular MRI segmentation. The architecture is based on the idea of network blocks in which each layer is densely connected with auxiliary side paths (skip connections) to all the following la...
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Deep Learning in Pulmonology
Deep learning has been successfully applied in various applications in pulmonary imaging, including CT registration, airway mapping, real time catheter navigation, and pulmonary nodule detection. Some of these applications are still in ongoing development, and here we review few of the most recen...
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