NoduleFlow: Automatic 3D Lung Nodule Detection and Segmentation

Key points

• Low dose Computed Tomography (CT) is the standard imaging modality for lung cancer screening.1
• On CT scans, lung cancer typically manifests as pulmonary nodules, whose manual identification is time-consuming and prone to observer variability, image quality limitations, and suboptimal reading conditions.1
• Deep learning-based approaches for the automatic detection and segmentation of lung nodules have demonstrated improved accuracy and efficiency.1
• We further investigate whether nodule localization benefits from the propagation of candidate regions across follow-up scans through inter-scan registration fields, as opposed to conventional independent per-scan analysis.1

Sources (1)

• [1]NoduleFlow: Automatic 3D Lung Nodule Detection and Segmentation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 10:30 AM

  In this work, we present NoduleFlow, a framework for lung nodule detection, classification, and segmentation directly from series of CT volumes.

  Low dose Computed Tomography (CT) is the standard imaging modality for lung cancer screening.

Extractive summary: sentences quoted from the sources.