A Cross Spatio-Temporal Pathology-based Lung Nodule Dataset

TL;DR

This paper introduces a novel cross spatio-temporal lung nodule dataset with pathological annotations, aiming to enhance CAD systems by capturing nodule evolution over time for better lung cancer diagnosis.

Contribution

It presents a new dataset that includes longitudinal CT sequences with pathological labels, addressing the gap in existing datasets focused on single-time-point imaging.

Findings

Dataset contains 328 CT sequences and 362 nodules from 109 patients.

Enables exploration of nodule progression patterns across time.

Supports development of more accurate and robust CAD methods.

Abstract

Recently, intelligent analysis of lung nodules with the assistant of computer aided detection (CAD) techniques can improve the accuracy rate of lung cancer diagnosis. However, existing CAD systems and pulmonary datasets mainly focus on Computed Tomography (CT) images from one single period, while ignoring the cross spatio-temporal features associated with the progression of nodules contained in imaging data from various captured periods of lung cancer. If the evolution patterns of nodules across various periods in the patients' CT sequences can be explored, it will play a crucial role in guiding the precise screening identification of lung cancer. Therefore, a cross spatio-temporal lung nodule dataset with pathological information for nodule identification and diagnosis is constructed, which contains 328 CT sequences and 362 annotated nodules from 109 patients. This comprehensive database is intended to drive research in the field of CAD towards more practical and robust methods, and also contribute to the further exploration of precision medicine related field. To ensure patient confidentiality, we have removed sensitive information from the dataset.