the Future in our
Certainly, the heart of innovative companies is the department of research & their development. In knowledge-based companies, the place of birth of each product can be searched in its R&D department.
the Future in our
Certainly the heart of innovative companies is the department of research & their development. In knowledge based companies, the place of birth of each product can be searched in its R&D department.
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Accordingly, from the first day of creation of the company, the research and development team was made up of the country's scientific elites. The purpose of this department was to build a smart ultrasound device using the Deep Learning systems.

Automated cardiac ventricular segmentation was introduced to the world using a new architecture of Deep-Learning networks called MFP-Unet. The results of this study are published in the authoritative journal European Journal of Medical Physics. This was done jointly with the Universities of Leuven Belgium and British Columbia Canada, under the supervision of the R&D department of MFP Co.
The potential to change the face of medical imaging is by two cutting edge technologies that are Artificial Intelligence (AI) and Augmented Reality (AR). These technologies have revolutionized image interpretation and visualization. Machine Learning algorithms can learn to see patterns similar to the way doctors see them, hence, they can support diagnosing procedure. On the other hand, AR makes a great promotion in the methods of displaying medical images for learners and surgeons.

At MFP, we provide intelligent solutions that help people to improve their healthy lifestyle, and increases the efficiency of medical imaging systems by using AI and AR. We aim to become the most influential company in the region by the year 2025. Regarding the growing nature of our projects, hard-working, intelligent people are always welcome to join us
Cardiac echo
Quantification of global and regional functional parameters of the cardiovascular system is a time-consuming and tedious task. We are developing an intelligent heart analysis system to extract chamber volumes, ejection fraction (EF), fractional area change (FAC), wall motion abnormalities, etc. from echocardiography sequences. Our deep learning-based system has a novel architecture, which we named it based on our company: MFP-Unet.
Fetal Biometry
The fetal biometry measurement encounters several challenges, including the presence of speckle, limited soft-tissue contrast, and difficulty in the presence of low amniotic fluid. We proposed a reliable system based on deep learning for automatic segmentation and measurement of fetal biometry parameters in ultrasound images. Our novel architecture, learns to extract fetal head, abdomen, and femur automatically as the object of interest. The system outperforms the state-of-the-art approaches in the segmentation of fetal ultrasound images and automatic measurement of biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), and femur length (FL).
Breast tumor
Ultrasound plays an essential role in breast disease diagnosis, follow up, and guided biopsy. Several efforts has been performed to classify breast tumors automatically. At MFP, we are developing a powerful image understanding system for distinguishing breast lesions and BI-RADS prediction.
The carotid intima-media thickness test (CIMT) is a measure used to diagnose the extent of carotid atherosclerotic vascular disease. The test measures the thickness of the inner two layers of the carotid artery—the intima and media—and alerts physicians to any thickening when patients are still asymptomatic. We have proposed a novel real-time system for automatic IMT measurement in our ultrasound machines.
Evaluation of the rigid-elastic properties of the vascular wall of the arteries and measurement of some parameters (such as Minimum vessel diameter per heart cycle, Changes in vessel diameter per cardiac cycle, Arterial tension index, Arterial stiness index, Modulus of elasticity, single-point pulse wave speed, and Growth Index) is a helpful tool for vessel analysis. We have provided these parameters in our ultrasound machines.
Our Latest Articles
NF-RCNN: Heart localization and right ventricle wall motion abnormality detection in cardiac MRI
A hybrid graph-based approach for right ventricle segmentation in cardiac MRI by long axis information transition