Abstract
Radiography is the primary modality for diagnosing canine hip dysplasia (CHD), with visual assessment of radiographic features sometimes used for accurate diagnosis. However, these features typically constitute small regions of interest (ROI) within the overall image, yet they hold vital diagnostic information and are crucial for pathological analysis. Consequently, automated detection of ROIs becomes a critical preprocessing step in classification or segmentation systems. By correctly extracting the ROIs, the efficiency of retrieval and identification of pathological signs can be significantly improved. In this research study, we employed the most recent iteration of the YOLO (version 8) model to detect hip joints in a dataset of 133 pelvic radiographs. The best-performing model achieved a mean average precision (mAP50:95) of 0.81, indicating highly accurate detection of hip regions. Importantly, this model displayed feasibility for training on a relatively small dataset and exhibited promising potential for various medical applications.
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Acknowledgments
This work was financed by project Dys4Vet (POCI-01–0247-FEDER-046914), co-financed by the European Regional Development Fund (ERDF) through COMPETE2020 - the Operational Programme for Competitiveness and Internationalisation (OPCI). The authors are also grateful for all the conditions made available by FCT- Portuguese Foundation for Science and Technology, under the projects UIDP/00772/2020, LA/P/0059/2020, and Scientific Employment Stimulus Institutional Call-CEECINST/00127/2018 UTAD.
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Loureiro, C. et al. (2024). Deep Learning-Based Hip Detection in Pelvic Radiographs. In: Pereira, A.I., Mendes, A., Fernandes, F.P., Pacheco, M.F., Coelho, J.P., Lima, J. (eds) Optimization, Learning Algorithms and Applications. OL2A 2023. Communications in Computer and Information Science, vol 1982 . Springer, Cham. https://doi.org/10.1007/978-3-031-53036-4_8
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