Research Article
Detection of Landmarks in X-Ray Images Through Deep Learning
@INPROCEEDINGS{10.1007/978-3-031-60665-6_20, author={Mauro Fernandes and Vitor Filipe and Ant\^{o}nio Sousa and Lio Gon\`{e}alves}, title={Detection of Landmarks in X-Ray Images Through Deep Learning}, proceedings={Wireless Mobile Communication and Healthcare. 12th EAI International Conference, MobiHealth 2023, Vila Real, Portugal, November 29-30, 2023 Proceedings}, proceedings_a={MOBIHEALTH}, year={2024}, month={6}, keywords={Automated Landmark Detection Deep Learning UNet Architecture FPN Architecture}, doi={10.1007/978-3-031-60665-6_20} }- Mauro Fernandes
Vitor Filipe
António Sousa
Lio Gonçalves
Year: 2024
Detection of Landmarks in X-Ray Images Through Deep Learning
MOBIHEALTH
Springer
DOI: 10.1007/978-3-031-60665-6_20
Abstract
This paper presents a study on the automated detection of landmarks in medical x-ray images using deep learning techniques. In this work we developed two neural networks based on semantic segmentation to automatically detect landmarks in x-ray images, using a dataset of 200 encephalogram images: the UNet architecture and the FPN architecture. The UNet and FPN architectures are compared and it can be concluded that the FPN model, with IoU=0.91, is more robust and accurate in predicting landmarks. The study also had the goal of direct application in a medical context of diagnosing the models and their predictions. Our research team also developed a metric analysis, based on the encephalograms in the dataset, on the type of Mandibular Occlusion of the patients, thus allowing a fast and accurate response in the identification and classification of a diagnosis. The paper highlights the potential of deep learning for automating the detection of anatomical landmarks in medical imaging, which can save time, improve diagnostic accuracy, and facilitate treatment planning. We hope to develop a universal model in the future, capable of evaluating any type of metric using image segmentation.
