RT journal article
T1 Automatic Lung Segmentation in Chest X-Ray Images Using SAM With Prompts From YOLO
A1 Khalili Khalili, Ebrahim
A1 Priego Torres, Blanca María
A1 León Jiménez, Antonio
A1 Sánchez Morillo, Daniel
A2 Ingeniería en AutomáticaElectrónica, Arquitectura y Redes de Computadores
K1 Biomedical X-ray imaging
K1 image segmentation
K1 lung
K1 deep learning
AB Despite the impressive performance of current deep learning models in the field of medicalimaging, transferring the lung segmentation task in X-ray images to clinical practice is still a pending task. In this study, the performance of a fully automatic framework for lung field segmentation in chest X-ray images was evaluated. The framework is rooted in the combination of the Segment Anything Model (SAM) with prompt capabilities, and the You Only Look Once (YOLO) model to provide effective prompts. Transfer learning, loss functions, and several validation strategies were thoroughly assessed. This provided a complete benchmark that enabled future research studies to fairly compare new segmentation strategies. The results achieved demonstrated significant robustness and generalization capability against the variability in sensors, populations, disease manifestations, device processing, and imaging conditions. The proposed framework was computationally efficient, could address bias in training over multiple datasets, and had the potential to be applied across other domains and modalities.
PB IEEE
SN 2169-3536
YR 2024
FD 2024-09-03
LK http://hdl.handle.net/10498/33435
UL http://hdl.handle.net/10498/33435
LA eng
NO Consejería de Universidad, Investigación e Innovación de la Junta de Andalucía (ProyExcel_00942)
DS Repositorio Institucional de la Universidad de Cádiz
RD 10-may-2026