AI service — AI Medical
Medical image segmentation
Turn CT, MRI and ultrasound studies into measurements — organ and lesion contours, volumes and margins — run as a managed AI pipeline in our GPU clusters, on a dedicated private cluster in our cloud, or installed on premise where imaging may not leave the building.
Send us your data volume, throughput and latency targets and any constraint we should design around. You get a proposed configuration, a benchmark on your own data, and a known cost per unit of work before you commit.
Segmentation replaces estimation with numbers. Every voxel is assigned to a structure, and results are written back as DICOM segmentation objects or NIfTI masks, with the volume, dimensions and location of each structure as data alongside. Your specialists get contours to review rather than contours to draw.
Use the models as they stand for standard anatomy, or have them fitted to the structures your own protocol defines. Work arrives from a PACS node, a watched folder or an API call, and every job records the model version behind it — so a measurement taken today is still reproducible a year from now.
Every output is clinical decision support, not a diagnosis: a qualified professional reviews and signs it. Where clinical use requires regulatory approval in your jurisdiction, that approval remains yours to hold — we provide the infrastructure, the model operations and the audit trail behind it.
What we size for
We build the medical image segmentation pipeline around the workload you actually have: data format, accuracy target, latency, throughput, concurrency, retention and scheduling. Start with a pilot, then scale production capacity without changing a line of your integration.
Common use cases
- Radiotherapy planning — Contour target volumes and organs at risk to shorten planning time.
- Volumetric follow-up — Measure how a tumour, lesion or organ volume changes between studies.
- Research cohorts — Segment a whole archive consistently so measurements are comparable across cases.
- Protocol-specific structures — Fit a model to structures your own annotation guide defines, not just standard anatomy.
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