An Efficient Neural Network for Segmenting Multiple Joint Tissues From Knee MRI With Hyperparameter Optimization: Data From the Osteoarthritis Initiative

• Main Authors: Shan Qing Yeoh, Pauline, Bing, Li, Goh, Siew Li, Hasikin, Khairunnisa, Wu, Xiang, Chai Hum, Yan, Kai Tee, Yee, Wee Lai, Khin
• Format: Article
• Published: Institute of Electrical and Electronics Engineers 2024
• Subjects: QA75 Electronic computers. Computer science; R Medicine (General)
• Online Access: http://eprints.um.edu.my/47115/; https://doi.org/10.1109/ACCESS.2024.3454374

Early detection of knee osteoarthritis is crucial because the damage in the knee joint is irreversible at the advanced stage. Medical images such as Magnetic Resonance Imaging plays an important role in knee osteoarthritis diagnosis as it provides excellent visualization of the osteoarthritis imaging biomarkers. Current clinical practice relies on manual inspection of the images which is very tedious, especially for 3D volumetric data. The overall aim of the study is to develop an efficient fully automated 3D segmentation model for segmenting multiple knee joint tissues from 3D Magnetic Resonance Imaging volumes. This study contributes by implementing hyperparameter optimization techniques to develop the optimal model for knee segmentation which will be beneficial for the detection of knee osteoarthritis. The model employs depthwise separable convolution for better computational efficiency. This paper presents an efficient model for knee bones and cartilages segmentation, modelled by Tree-of-Parzen-Estimators algorithm, which achieved an average dice score of 0.939 and a Jaccard index of 0.891. Our model outperformed 3D U-Net and 3D V-Net by approximately 7% and 6% respectively in terms of Dice Similarity Coefficient, with remarkably less computations, using the same dataset. The efficient model enhances the segmentation of the knee structures for better visualization, which contributes to a more accurate diagnosis in clinical practice. It also reduces the computational cost, allowing more possible adaptation of 3D neural networks in real-world clinical settings. Therefore, this work contributes to advance medical imaging and diagnostics while also holds the potential to improve clinical practice.