3DSegmaUNet
AI-Powered 3D Medical Imaging & Volumetric Liver Segmentation
3DSegmaUNet is an end-to-end medical AI application engineered for automated 3D volumetric liver and lesion segmentation from abdominal DICOM CT scans. Utilizing 3D U-Net neural architectures and MONAI medical frameworks, 3DSegmaUNet processes multi-slice radiological volumes to generate precise 3D organ mesh geometries for surgical planning and PACS diagnostic workflows.
Clinical AI Stack Architecture
Python 3.10
Medical Pipeline
PyTorch
3D Neural Net
MONAI
Medical Deep Learning
SimpleITK
DICOM Image Processing
Flask API
Inference Microservice
Docker
PACS Container
3D Slicer
Volumetric Viz
Key Clinical Capabilities
Automated 3D Volumetric Segmentation
Segments organ volumes directly from multi-slice CT/MRI DICOM scans, replacing manual slice-by-slice contouring.
High Dice Similarity Coefficients
Achieves high overlap accuracy against ground-truth radiologist annotations for organ and lesion boundaries.
40% Decrease in Contour Time
Reduces radiological review and pre-operative liver resection planning time from hours to seconds.
PACS & 3D Slicer Integration
Exports 3D mesh files directly to PACS systems and 3D Slicer workstations for surgical navigation.
Built For Clinical Teams
Radiologists & Imaging Centers
Automate volumetric measurement reports for abdominal CT and MRI scans.
Surgical Operations Teams
Reconstruct 3D organ geometries for pre-operative liver resection planning.
Biomedical AI Researchers
Benchmark MONAI-based 3D U-Net architectures on clinical DICOM datasets.
Related Platforms
Interested in clinical evaluation for 3DSegmaUNet?
Connect with our biomedical AI engineering team to partner on clinical trial deployments or DICOM integration.
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