Dataset for Segmentation and Classification of Cardiac Implantable Electronic Devices in Chest X-Rays

This dataset encompasses a comprehensive collection of 2,321 chest radiographs
(originally DICOM, converted to PNG) and 11,072 smartphone images documenting
various cardiac implantable electronic devices (CIEDs) such as implantable
pacemakers, cardioverter defibrillators, cardiac resynchronization therapy
devices, and cardiac monitors, collected from 896 patients at the Charite \-
Universitatsmedizin Berlin over a decade (January 2012 to January 2022). The
dataset's primary objective is to advance the development and validation of
automated techniques for precise segmentation and classification of CIEDs
across both traditional and smartphone-generated imagery. It includes a
diverse array of 27 CIED models from four manufacturers (Medtronic, Biotronic,
Boston Scientific, Abbott Laboratories including St. Jude Medical), captured
in anterior-posterior or posterior-anterior chest radiographs using five
different smartphone brands. This dataset aims to underpin the creation of
deep learning models for accurately identifying CIED types, manufacturers, and
models. Designed to be a robust foundation for research and clinical
applications, it facilitates the exploration of innovative machine learning
solutions for CIED identification leveraging both original radiogrpahs and
smartphone images, thereby addressing a critical need in cardiac care
technology.

本数据集包含共计2321张胸部X光片（原始格式为DICOM，已转换为PNG格式）与11072张智能手机拍摄影像，用于记录各类心脏植入式电子设备（cardiac implantable electronic devices，CIEDs），包括植入式起搏器、心脏复律除颤器、心脏再同步治疗设备以及心脏监护仪。该数据集采集自柏林夏里特医学院（Charité – Universitätsmedizin Berlin）896名患者，时间跨度超过十年（2012年1月至2022年1月）。本数据集的核心目标是推动自动化技术的开发与验证，以实现对传统影像与智能手机拍摄影像中心脏植入式电子设备的精准分割与分类。数据集涵盖了来自四家制造商（美敦力、百多力、波士顿科学、雅培实验室（含圣犹达医疗））的27种不同型号的CIED，拍摄方式包括前后位或后前位胸部X光片，同时使用了五个不同品牌的智能手机进行影像采集。本数据集旨在支撑深度学习模型的构建，以精准识别CIED的类型、制造商及具体型号。作为适用于研究与临床应用的可靠基础数据集，该数据集可助力探索基于原始X光片与智能手机影像的新型机器学习解决方案，用于心脏植入式电子设备识别，从而填补心脏护理技术领域的一项关键空白。