Clinical Imaging Infrastructure • Volumetric Imaging
3D imaging reliability requires volumetric QA — not 2D labeling at scale.
CT/MRI/PET/3D ultrasound fails when slice-level annotation ignores 3D continuity, protocols drift, or QC is not volumetric. Trinzz builds 3D-first pipelines: volumetric SOPs, continuity/topology checks, multi-expert consensus, and release gates — so models generalize across scanners, sites, and time.
SOC 2 Type II
ISO 27001:2022
HIPAA compliant
PHI controls + audit logs
Versioned releases
Imaging annotation — engineered for deployment
Trinzz turns imaging annotation into a governed reliability system: AI assists, experts validate, agreement is quantified, QC is systematic, and releases ship with evidence.
Clinical‑grade ground truthExpert validation + adjudication + consensus confidence — per case.
Reliability scoring (CG‑DRS)Release gates backed by quality, traceability, and drift readiness metrics.
Evidence PackQC report, consensus report, lineage export, monitoring spec.
Monitoring loopDrift triggers + edge‑case loopback to continuously improve.
Clinical‑grade lifecycle: model assist + expert validation + QC/consensus + release gate.
3D‑first reliability system
Volumetric QA that prevents continuity failures
This is what turns a dataset into something procurement + clinical validation can sign off on.
TraceabilityConsensusQCMonitoring
Volumetric SOP Enforcement3D labeling standards applied across slices, phases, and sequences.
3D Consistency QCTopology/continuity checks and volume-level sanity metrics.
Protocol GovernanceSequence/phase metadata validated against acquisition specs.
Subgroup RobustnessWorst-group gaps (site/scanner/protocol) measured and remediated.
Shift Readiness MonitoringBaseline distributions per protocol + drift triggers when acquisition mix changes.
Release Gates + Evidence PackVersioned 3D releases with manifests, QC/consensus reports, monitoring spec.
3D QA checklist
What "volumetric QC" actually means
This is why 3D datasets can't be treated like 2D labeling at scale.
TopologyContinuityProtocol bins
Continuity checksSlice-to-slice consistency, hole detection, discontinuity flags.
Topology + geometryAnatomical plausibility checks on masks and volumes.
Protocol governanceContrast phases/sequences validated; drift triggers when acquisition mix shifts.
Deployability lever: we quantify worst-protocol and worst-site gaps before models meet real hospitals.
Use cases
Where this should convert
Written for enterprise imaging teams evaluating deployment readiness.
HospitalsDiagnostics networksMed‑AI enterprises
Organ & lesion segmentationVolume-first QC prevents discontinuities and inconsistent masks.
Multi-phase CT/MRI programsGovern phases/sequences so training is defensible and reproducible.
Protocol/scanner generalizationTrack protocol bins and reduce worst-site failures.
Production monitoring readinessDefine triggers before deployment to prevent silent degradation.
Benchmarking vendor modelsEvaluate dataset reliability before comparisons.
3D foundation model pretrainingCurate large 3D corpora with strict provenance and QC.
Evidence pack
What you get with every release
Evidence reduces “trust debates” to artifacts.
QC reportConsensus reportLineage exportMonitoring spec
- Volumetric manifest + version hash + deterministic release notes
- 3D QC report: topology/continuity checks, volumetric sanity metrics, pass rates
- Consensus report: multi-expert adjudication + confidence measures
- Protocol governance memo: phase/sequence metadata + SOP mapping
- Subgroup robustness report: worst-group gaps across bins
- Monitoring spec: drift thresholds for protocol mix and acquisition changes
Release Gate: Pass / Conditional / Fail based on QC strength, consensus confidence, and traceability completeness.
Reliability is not a claim. It's a tracked system — with thresholds, evidence, and release discipline.
Request a Reliability Audit
Share a dataset manifest. We return CG‑DRS diagnostic + validation plan + monitoring triggers.