800 CTPA studies with pulmonary embolism segmentation masks and clot-burden scoring for AI detection

Overview

We are developing a deep-learning system for automated detection and clot-burden quantification of acute pulmonary embolism (PE) on CT pulmonary angiography (CTPA). PE manifests as intraluminal filling defects within pulmonary arteries, typically appearing as low-attenuation regions (–20 to +80 HU) surrounded by contrast-enhanced blood (200–400 HU at peak enhancement). Accurate segmentation of emboli from the main, lobar, segmental, and subsegmental pulmonary arteries is the central annotation task. Required imaging protocol: CTPA acquired with bolus-tracked contrast injection (iodinated contrast, 100–120 mL at 4–5 mL/s), slice thickness ≤1.25 mm, reconstructed in mediastinal window (WL 40 HU, WW 400 HU) and lung window (WL –600 HU, WW 1500 HU). Each study must include a complete volumetric DICOM series with full coverage from the lung apices to the costophrenic angles. Incidental findings such as pleural effusion, right heart strain (RV/LV ratio ≥0.9), and pulmonary infarct should be flagged in the JSON metadata but do not require pixel-level annotation. Tube voltage should be 100–120 kVp with automated tube-current modulation; studies acquired at non-standard voltages must include CTDI vol and DLP values in DICOM metadata for dose normalization. Volumetric series must be isotropic or near-isotropic (≤1.25 mm reconstructed slice thickness) to enable accurate three-dimensional vessel-tree segmentation and embolus localization. Annotation requirements: 3D segmentation masks of all emboli, centreline labeling of affected vessel segments, and a computed modified Miller index (mMI) or Qanadli clot-burden score. Negative CTPA studies (no PE) should constitute at least 30% of the dataset to enable specificity optimization. Cases confirmed by ventilation-perfusion (V/Q) scan or catheter angiography are particularly valuable and should be flagged accordingly. Inter-rater agreement for embolus segmentation must reach a Dice coefficient of ≥0.70 on lobar and segmental arteries; subsegmental PE cases may be annotated by consensus read given known inter-observer variability. Scanner balance across GE, Siemens, and Philips CTPA protocols is requested, and at least 15% of studies should originate from institutions in different countries to capture contrast injection protocol variations. QA exclusion criteria include studies with inadequate arterial opacification (main pulmonary artery attenuation below 200 HU), respiratory motion artifact degrading vessel conspicuity, or missing coverage of the pulmonary arterial trunk. De-identification per HIPAA Safe Harbor and DICOM PS3.15, with consistent pseudonymization for patients with follow-up imaging. Delivery in NIfTI-2 format with DICOM originals included is preferred. JSON sidecars must encode PE acuity (acute vs. chronic), Wells score, D-dimer value, and outcome (30-day mortality, need for thrombolysis) where available. The algorithm will be validated for integration into emergency radiology AI triage workflows and submitted for regulatory clearance. A formal data-use agreement and institutional ethics approval are prerequisites before data transfer commences.

Progress

Data Specifications

Use Cases

• Training and validating Medical imaging AI/ML models
• Benchmarking Medical imaging detection and segmentation algorithms
• Building de-identified Medical imaging research datasets for academic studies
• Augmenting existing Medical imaging datasets to reduce class imbalance