SEEING BEYOND THE VISIBLE FUTURE HEALTHCARE www.sensai.eu 2 S e n - - PowerPoint PPT Presentation
SEEING BEYOND THE VISIBLE FUTURE HEALTHCARE www.sensai.eu 2 S e n s . A I | 2 0 1 8 ABOUT FUTURE PROCESSING Our main goal and the central part of our operations is the use of software to offer solutions to business problems. The company
www.sensai.eu
SEEING BEYOND THE VISIBLE
FUTURE HEALTHCARE
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ABOUT FUTURE PROCESSING
Our main goal and the central part of our operations is the use of software to offer solutions to business problems. ▪ The company was founded in 2000 by Jarosław Czaja (CEO). ▪ Revenues above the level of PLN 104 million (2017). ▪ 900 people. ▪ 18 years of experience. ▪ 150 clients in portfolio. ▪ R&D project management. ▪ Development of our own products.
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OUR COMPETENCES
Our experts will support the customer’s projects and R&D departments with their knowledge and competences Future Processing has more than 10 years of experience in the development of IT solutions that support medical imaging
YEARS OF EXPERIENCE
EXPERTS
We have 69 experts specialized in computer vision There are machine learning and medical imaging specialists working for us
SPECIALISTS SUPPORT
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WE WORK IN ACCORDANCE WITH ISO
Our specialists have knowledge of standards applicable in the medical industry. ▪ We meet the requirements of the ISO 13485 standard, as confirmed by the Certificate for the Management System according to ISO 13485:2016. ▪ Knowledge of standards ISO 13485 and 14971 and the ability to create software while complying with the 62304 standard allows us to cooperate with institutions and authorities in the field of medical diagnostics.
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Sens.AI (Enhancing the diagnostic efficiency
imaging in personalised Oncology by extracting New and Improved Biomarkers)
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Seeing beyond the visible
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WHAT IS Sens.AI?
Sens.AI is a system for comprehensive and automatic DCE (dynamic contrast-enhanced imaging) analysis. ▪ It is a tool supporting the diagnosis of brain lesions through the analysis of magnetic resonance images after contrast enhancement. ▪ Sens.AI analyzes sequences of medical images in search of relevant diagnostic information. ▪ The purpose of this is:
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CLINICAL PARTNER
Sens.AI is being created in close cooperation with:
Oncology Center – Institute named after Maria Skłodowska-Curie, Branch in Gliwice (Centrum Onkologii – Instytut im. Marii Skłodowskiej-Curie, Oddział w Gliwicach)
Gliwice Center of Oncology (Centrum Onkologii) is a multidisciplinary oncological center offering cancer patients all the highly specialized methods of combination therapy of all types of cancer that are recognized in the world. The Sens.AI project is carried out with the Department of Radiology and Imaging Diagnostics of the Oncology Center.
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MULTIDISCIPLINARY TEAM
This team deals with the design and analysis (theoretical and experimental)
scientists (doctors of physical and technical sciences).
RESEARCH TEAM
Consisting of experienced software engineers – this team works on the implementation of the final product. (RESEARCH)
DEVELOPMENT TEAM
Two teams of experts are working on the Sens.AI system:
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DESCRIPTION OF THE SOLUTION
▪ It is a tool supporting the diagnosis of neoplastic lesions. ▪ Performing of analysis of medical images from magnetic resonance (MR) after contrast enhancement. ▪ Following contrast-enhanced MRI, the images obtained are analysed by Sens.AI. The system performs automated analysis of imaging data by performing image segmentation and analysis of contrast flow in brain
interpret report in the form of DICOM files. ▪ The entire analysis process is carried out without user’s intervention.
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INNOVATIVE ALGORITHMS
▪ Automatic and very fast DCE analysis of medical images is possible thanks to the innovative algorithms created by Future Processing using machine learning techniques, especially deep learning. ▪ Algorithms, using machine learning techniques, allow effective analysis of a series of medical images, obtained during magnetic resonance imaging. ▪ The report (in DICOM format) includes segmented neoplastic lesions in dynamic brain imaging, contrast flow graphs, parametric maps and easy to interpret and analyze biomarkers.
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POSSIBILITY OF RISK ASSESSMENT
▪ Biomarkers, graphs and parametric maps of the brain can be used to analyze the characteristics and stage of development
cancer. ▪ Thanks to this, the medical staff receives support in the process of diagnosing the patient. ▪ The medical staff interprets the results and makes the final diagnosis.
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WORKFLOW
MAGNETIC RESONANCE IMAGING AUTOMATIC SEGMENTATION AND ANALYSIS INTERPRETATION OF RESULTS BY A RADIOLOGIST REPORT ON ANALYSIS
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SEGMENTATION - EXAMPLE
Neoplastic lesion imaging in MR head image
2018 (in review). Pixels correctly classified as tumor are highlighted in yellow, false negatives in green, and false positives in red.
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PARAMETRIC MAPS - EXAMPLE
Ktrans – the first and most important DCE parameter; the volume transfer constant for gadolinium between blood plasma and the tissue extravascular extracellular space (EES). Like a chemical reaction rate, its units are given in values of (1/time), such as min−1. In brief, Ktrans reflects the sum of all processes (predominantly blood flow and capillary leakage) that determine the rate of gadolinium influx from plasma into the EES.*
*References
Ferrier MC, Sarin H, Fung SH, et al. Validation of dynamic contrast-enhanced magnetic resonance imaging-derived vascular permeability measurements using quantitative autoradiography in the RG2 rat brain tumor model. Neoplasia 2007; 9:546-555. (Good correlation between Ktrans and autoradiographically measured influx rate in a rat glioma model with high permeability). Tofts PS. T1-weighted DCE imaging concepts: modelling, acquisition and analysis. MAGNETOM Flash 2010; 3:30-35.
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CONTRAST CONCENTRATION GRAPHS IN TUMOR TISSUE AND IN AIF - EXAMPLE
Sample graphs of contrast saturation - orange dots indicate the average concentration of contrast in tumor tissue (the graph displays the model adjustment), and pink dots – the average concentration of contrast in AIF (artery input function; the graph presents the model adjustment).
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REPORT
Sample report (in DICOM format) generated by the Sens.AI system.
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▪ Automatic execution of both segmentation and analysis of medical images for each patient. ▪ Time saving – the system eliminates the necessity of manual segmentation and image analysis. ▪ DCE analysis and results in real time. ▪ Support for the diagnosis and prediction process. The results of the algorithm are not affected by the error of the human eye. ▪ Processing and selection of data for analysis without user’s intervention. ▪ Repeatability of results obtained during the segmentation and DEC analysis conducted. ▪ Analysis and comparison of results possible with use of publicly available DICOM file viewers.
ADVANTAGES
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archiving and communication (PACS) system, as well as algorithms for segmentation and DCE analysis of MR images after contrast enhancement
▪ segmentation of neoplastic lesions in the brain and arteries from medical images coming from magnetic resonance (MR) after contrast enhancement – for customers who are looking for a modern tool for segmentation, ▪ segmentation of various organs (imaged in various modalities, such as MR, CT or PET), ▪ DCE analysis – for customers who already have a segmentation tool and want to extend it with automatic and fast DCE analysis.
methods in medical applications. 4. Support of specialists in the field of machine learning combining academic competencies with technological skills.
WHAT DO WE OFFER?
Future Processing
44-100 Gliwice, Poland +48 32 438 43 06
sensai@future-processing.com www.sensai.eu
What can we do together?
Contact us and find out how
project. www.sensai.eu