DOI: 10.6084/m9.figshare.31079236.v1
title: MIQR-CC Dataset

description: <b>Description</b>Endoscopic Retrograde Cholangiopancreatography (ERCP) is a key procedure for diagnosing and treating biliary and pancreatic diseases. Artificial intelligence methods have shown promise in automating ERCP image interpretation; however, publicly available ERCP datasets remain scarce. This collection aims to address this limitation by providing a large, curated, and clinically annotated ERCP fluoroscopy image dataset to support research in automatic image analysis and diagnosis.<b>Dataset Overview</b><b>Total patients:</b> 1,602<b>Raw images:</b> 19,018 PNG images<b>Processed images:</b> 19,317 PNG images<b>Labeled images:</b> 5,519 images with expert annotations<b>Image modality:</b> Fluoroscopy during ERCP procedures<b>Data format:</b> PNG images + CSV metadata file<b>Clinical Content</b>The dataset includes all fluoroscopic images acquired throughout the ERCP workflow, encompassing pre-cannulation images used for anatomical orientation and procedural planning, cholangiographic images obtained during contrast injection and therapeutic maneuvers, as well as post-procedural images documenting the final outcome of the intervention.Annotated images include clinically relevant categories:Biliary lithiasisBiliary leaksBenign stricturesMalignant stricturesNormal findings<b>Annotation Process</b>All labeled images were manually inspected and annotated by two gastroenterologists with more than 5 years of ERCP experience and over 400 procedures annually. Annotations were independently reviewed by a senior gastroenterologist with more than 20 years of experience to ensure diagnostic reliability.<b>Data Acquisition</b>Images were retrospectively extracted from the SECTRA PACS system of a tertiary care high-volume ERCP center performing over 750 procedures annually. Procedures were conducted by experienced gastroenterologists, and fluoroscopic acquisition was performed by trained radiology technicians. Exams were independently reviewed by a second gastroenterologist for consistency.<br>Fluoroscopic systems used include Ziehm Vision RFD 3D and Philips PCR Eleva units, introducing natural variability in image characteristics.<b>Data Processing</b>A multi-stage Python-based pipeline was applied to:Export DICOM filesAnonymize all patient-identifiable informationConvert DICOM to PNGPartition raw fluoroscopic frames into individual processed imagesTwo data folders are provided:<b>Raw:</b> Original anonymized fluoroscopy frames<b>Processed:</b> Cropped sub-images extracted from raw framesA metadata file (<code>metadata.csv</code>) links each processed image to its corresponding raw image and includes anonymized patient-level and image-level descriptors. Each row corresponds to one processed image.<b>Potential Applications</b>This dataset supports research in:Automatic classification of ERCP findingsAI-assisted diagnosis of biliary and pancreatic diseasesImage-based analysis of therapeutic endoscopyFully supervised, semi-supervised, and weakly supervised learning approaches<b>Ethics Approval</b>The study was approved by the Board of Directors of Unidade Local de Saúde do Alto Minho (ULSAM).<br>Approval ID: <b>243/CA-2025</b><br>All data were retrospectively collected and fully anonymized.