2,500 abdomen-pelvis CT volumes for kidney stone detection, stone composition classification, and urolithiasis burden scoring

Overview

Our urology AI research group is assembling a comprehensive urolithiasis dataset to train models that automatically detect, localize, size, and classify renal and ureteral stones on non-contrast CT of the abdomen and pelvis (NCCT-KUB protocol). Kidney stones typically present as hyperdense foci (200–1000+ HU depending on composition; uric acid stones appear at lower density 200–400 HU, while calcium oxalate stones exceed 800 HU), making NCCT the gold-standard imaging modality for urolithiasis evaluation. Accurate Hounsfield unit measurement is essential for predicting stone composition and guiding treatment selection (shock-wave lithotripsy vs. ureteroscopy vs. percutaneous nephrolithotomy). Imaging protocol: NCCT acquisitions at 120 kVp (or low-dose 80–100 kVp protocols acceptable), slice thickness ≤2.5 mm, reconstructed in both soft-tissue window (WL 40 HU, WW 400 HU) and bone window (WL 400 HU, WW 1800 HU) for stone conspicuity. Coronal and sagittal MPR series are strongly encouraged. Each study must cover from the superior poles of the kidneys through the urinary bladder (ureterovesical junction). Dual-energy CT studies with stone composition maps are particularly valuable and should be flagged separately. Volumetric DICOM series are required; thin-section reconstructions at ≤1.25 mm for dual-energy cases enable virtual monochromatic image generation at 40–70 keV for optimal stone conspicuity and composition discrimination. Annotation requirements: bounding-box localization for each stone with anatomical location label (upper/mid/lower pole calyx, renal pelvis, proximal/mid/distal ureter, bladder), maximum axial stone diameter in millimeters per RECIST convention, mean HU value, and a stone composition prediction label (calcium oxalate, calcium phosphate, uric acid, struvite, cystine, or mixed) where dual-energy or prior metabolic workup data are available. Total stone burden (number and cumulative volume in cubic millimeters) should be recorded per patient in the JSON sidecar. Negative studies (no stone) must account for at least 25% of the dataset. Inter-rater agreement for stone localization (bounding-box IoU ≥ 0.60) and mean HU measurement (within ±50 HU) must be reported. QA exclusion criteria include scans with severe streak artifact from bilateral hip prostheses obscuring the ureters, incomplete coverage of the KUB field, or absence of acquisition kVp in DICOM metadata. De-identification per HIPAA Safe Harbor; ureteral stent or nephrostomy tube presence must be flagged in the JSON sidecar as these hardware items directly affect stone visibility and HU measurement accuracy. Acceptable formats are DICOM and NIfTI. The trained model targets integration into automated radiology reporting pipelines to generate structured urolithiasis reports, reducing radiologist workload while improving measurement reproducibility. Scanner diversity across GE, Siemens, Philips, and United Imaging platforms is required; contributions from institutions in multiple geographic regions are welcome to capture dietary and demographic variation in stone epidemiology, as stone composition prevalence differs markedly between Western and East Asian populations.

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