• Automated body compositions segmentation
• Contrast/Non-contrast, 2D/3D modes
• Overall accuracy: Approx. 97%
• Automated L3 level, abdominal waist detection
• User define options

Redefine your body composition in 3D. Deep learning algorithms enable you to analyze the body composition within few minutes (3mins for wholebody, 2mins for abdomen). Distributions of adipose tissues and muscles in 3D offer you valuable medical data for numerous applications.

Value Proposition

Highly referred to have an accurate quantitative data for Sarcopenia, Osteoporosis, Obesity, Hepatic diseases, and Cancer from CT images. These wide range applicable diseases can be handled for significant treatment with seven body compositions boundary data from this model, DeepCatch, which segments bone, central nervous system, internal organs, abdominal visceral fat, muscle, subcutaneous fat, and skin.

Narrative

Chect out this "Body compositions detection within 2 minutes on CT": https://www.youtube.com/watch?v=cJM16CQJjx0&ab\_channel=MEDICALIP

Intended Use

For only CTR image analysis on DeepCatch Solution

Limitations

Only Window OS, High GPU requirement

Model performance metrics

Skin 90.0(2D) 96.0(3D) Bone 97.2 98.6 Muscle 95.7 98.0 Abdominal visceral fat 91.1 96.8 Subcutaneous fat 95.8 98.3 Internal organs 93.2 96.8 Central nevous system 92.3 97.7 97.6

MEDICAL IP

MEDICAL IP

Here at MEDICALIP we are leading the future of healthcare with innovative technologies. We are developing various medical imaging analysis technology based on AI and medical 3D printing technology to reduce the time and cost of medical staff and the pain of patients. We are creating a future that will enable a better medical experience and service.

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