A Two-Stage U-Net framework for Interactive Segmentation of lung nodules in CT scans

• Type: Article in International Scientific Journal
• Date: 2025

Authors

Fernandes, L (Author) Other; Pereira, T (Author) Other; Oliveira, HP (Author) FCUP

Journal

Title: IEEE Access

Vol. 13

ISSN: 2169-3536

Publisher: IEEE

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-018-RZ2

DOI: 10.1109/access.2025.3566203

Abstract (EN): Segmentation of lung nodules in CT images is an important step during the clinical evaluation of patients with lung cancer. Furthermore, early assessment of the cancer is crucial to increase the overall survival chances of patients with such disease, and the segmentation of lung nodules can help detect the cancer in its early stages. Consequently, there are many works in the literature that explore the use of neural networks for the segmentation of lung nodules. However, these frameworks tend to rely on accurate labelling of the nodule centre to then crop the input image. Although such works are able to achieve remarkable results, they do not take into account that the healthcare professional may fail to correctly label the centre of the nodule. Therefore, in this work, we propose a new framework based on the U-Net model that allows to correct such inaccuracies in an interactive fashion. It is composed of two U-Net models in cascade, where the first model is used to predict a rough estimation of the lung nodule location and the second model refines the generated segmentation mask. Our results show that the proposed framework is able to be more robust than the studied baselines. Furthermore, it is able to achieve state-of-the-art performance, reaching a Dice of 91.12% when trained and tested on the LIDC-IDRI public dataset. © 2013 IEEE.

Language: English

Type (Professor's evaluation): Scientific