FUJIFILM

The technology controls image quality based on a statistical model, an object model and a physical model using iterative processing.
This technology provides a high visibility image even at a low radiation dose , by maintaining the original texture of the image at a high denoise condition.
It brings both “high visibility at low dose” and “original texture at high noise reduction”

We are developing the AI image reconstruction technology that removes noise components of image by preventing the structure and the contrast from degrading by a high speed or a high resolution MR scanning. As a result, the technology shortens the scan time with keeping the image quality, or improves the visibility of the image by improving the image quality.

This flitering technology with AI suppresses aliasing noises which occur in extended images.

This technology uses AI to distinguish between echo signals and noises, and extracts the signals necessary for diagnosis. This enables ultrasound systems to provide high-quality images even in difficult cases.

The technology virtually generates thin slices from thick slices. It can be applied to the whole body, useful for utilizing past data. It brings high visibility in the VR display and reconstructed sagittal/coronal images.

AI technology to segment and quantify the volume of each brain regions.
This technology can be used for pre-surgery simulation or calculation of the atrophy rate for each region between past and current exams.

This technology visualizes the bone density temporal difference by performing image registration between the past and current image of the same patient.
The increase and decrease in CT value will be highlighted.

We are developing a technology to recognize urinary bladder area and measure the volume automatically. It will make it possible to measure residual urinary easily, and support urination management at home medical care.

This technology which developed by deep learning will automatically detect the lumbar spine and femur bone that enhanced by energy subtraction, and calculate the bone mineral density.

Automatically extracts the pancreas, each surrounding organ area, and surrounding vessels from contrast-enhanced CT images and analyzes them in 3D.
The pancreatic duct diameter, residual pancreatic volume, measurement of the cross-sectional area, and resection simulation can be performed to support pancreatic surgery.

AI technology to extract each kidney and surrounding vessels from CT or MRI images assists volume calculation of renal and compare current and past results.
This technology can also precisely extract cystic kidneys, a genetic disease that is often difficult to extract due to the diversity of shapes, and assist diagnosis and treatment.

AI technology to automatically extract liver vessels and then labels liver according to the anatomic liver segments (Couinaud segments) based on the vessel running.
This can be used for Interventional Radiology planning for liver.

Technology that automatically extracts the liver parenchyma and surrounding vessels from CT images and analyzes them in 3D.
This technology can assist preoperative simulation by planning resection area based on the calculation of the dominant vessels.

AI technology to automatically extract lung fields, 5 lobes, and bronchus,　surrounding vessels from CT images.
From each extraction result, the percentage of low attenuation area is calculated.
These are expected to contribute the diagnosis of COPD.

This technology automatically extracts the femur, tibia, patella, cartilage, and meniscus from MRI images.
By measuring the thickness of the cartilage and the distance between the meniscus and tibia, it is possible to evaluate the condition of the knee joint without invasion.

Technology to recognize each vertebral body individually and label the vertebrae and ribs. This reduces the burden on the physician of visually counting bone positions. It is also used for visualization of fractures and bone metastasis by subtraction of rigid body alignment of vertebral bodies.

AI technology to subdivide lung into 10 segments in right lung, 8 segments in left lung from CT images. It can be used for confirming the position of chest nodules detected by a doctor.

AI technology to segment relatively large mediastinum, axillary lymph node from CT images. This technology can be used for assessment of lymph node enlargement.

AI technology to detect a suspcious rib fracture from CT images. This technology will assist prevention of overlooking of subtle rib fracture.

AI technology to identify suspicious region with COVID-19 related findings from CT images. This technology will help doctors diagnose efficiently.

This technology assists real time detection and characterization of colonic polyps from colonoscopy images with AI software.

AI technology to detect and quantify suspicious lesion from CT Images.
This will assit prevention of overlooking of nodule and generation of language of findings for radiology report.

This technology identifies various findings of interstitial pneumonia that appear on CT images, such as consolidation, reticular pattern, ground glass opacity and honeycomb, and calculates their distribution and volume.
This will assist in the diagnosis of the severity and therapeutic efficacy of interstitial pneumonia, which are conventionally performed qualitatively, by providing a quantitative value for assessment.

The technology which prepares environment on the modality to exapnd the opportuities of AI-CAD applications and improve workflow not only in hospitals, but also at point of care.

This technology extracts high-signal and low-signal areas in head CT images by comparing the left and right sides of the brain region. Generally, high-signal and low-signal areas are used to evaluate the state of hemorrhage and ischemia in the brain for stroke diagnosis. This will assist the diagnosis of head CT imaging.

The technology to extract and highlight areas of higher or lower absorption than the surrounding tissue. For example, high/low absorption in the liver, high absorption in the thoracic cavity, and low absorption in the kidney may be informative in determining the findings of each part. In the future, we aim to highlight both contrast-enhanced and non-contrast-enhanced images.

The technology detects three types of imaging findings: nodule, consolidation, and pneumothorax from chest X-ray images. It is expected to contribute to preventing oversights in various chest X-ray examinations, such as health checkups and routine medical examinations.

This technology highlights areas in the B-mode image where luminance characteristics are different from the surroundings

This technology extracts the target area from the image.
Positioning during imaging is performed automatically.
Unnecessary tissue is automatically removed before creating a 3D image for diagnosis.
It is a technology that supports the automation of inspections.

The technology enables automatic recognition of left ventricle, left atrium and right atrium wall in addition to view recognition of the heart, and automatic measurements realize autonomous measurements of ejection fraction and volumes.
The measurement function are used very often in echocardiography exams. This AI technology assist to reduce inter- and intra-observer variability so that improve reproducibility and efficiency of exams.

This technology assists real time detection and distinction between an artery and a vein, as well as measurement of diameter and depth of the vessel from ultrasound images. This will streamline Point Of Care UltraSound exams.

This AI technology detects foreign objects in X-ray images. This technology can be applied to the detection of surgical objects such as gauze and reduce the burden on doctors to check the surgical objects held at each surgical procedure.

This technology is to assist positioning before exposure by images from camera which implemented on the X-ray modality and confirming the more optimal positioin with AI software .

The sensor integrated in the X-ray system will recognize three-dimensional information from the patient's body.
It will then automatically suggest the optimized exposure conditions and adapt proper image processing.

We developed AI algorithms to predict clinically important outcomes using inpatient data with pneumonia aggregated throught our integrated medical care support platform system. This technology will assist preparing personalized medical documents and optimizing medical resources

The technology supports populating radiology report of lung nodules based on the observation and analysis results of the properties. It enables to analyze multiple properties such as shape, absorption, presence of calcification, etc., and provide multiple finding sentence candidates using natural language processing technology.

The technology analyzes the properties of areas of interest in the liver and assists doctors in writing radiology report. It is possible to analyze not only the location and size, but also the appearance/disappearance of enhancement based on multi-phase images of contrast-enhanced CT. In addition, similar to lung nodule characterization analysis, it is also possible to present multiple finding sentence candidates using natural language processing technology.

The technology estimates high-value threshold of the region of interest, and quantify high intensity area in lung field. For example, it is expected to offer information for quantitative analysis of partially solid nodules.

This technology automatically measures EvansIndex, corpus callosum angle, and MidlineShift from head CT. EvansIndex and corpus callosum angle are expected to support the evaluation of hydrocephalus and MidlineShift to evaluate head trauma.

The technology under development quantitative detection of slight brain volume loss and iron deposition in the early stages of dementia using hybrid analysis of QSM（Quantitative Susceptibility Mapping）and VBM（Voxel Based Morphometry）in MRI. This will assist the diagnosis of dementia.