A Comparison of CNN Models for Automated Femur Segmentation Based on DICOM Images

Segmentation of anatomical components is a critical step in creating accurate and realistic 3D models of the human body, which are employed in a wide range of clinical applications, particularly in orthopedics. Recently, many deep learning approaches have been proposed to solve the problem of manual segmentation. Among the available software for automatic segmentation, MONAI Label is a free open-source tool, which allows for the creation of annotated datasets and the development of AI-based annotation models for clinical assessment. In this context, the present study is designed to compare the performance of two well-known neural networks in segmenting knee bones. In spite of the fact that several studies have investigated the use of deep learning techniques for knee reconstruction, there is no consensus regarding the most effective method. In the present study, validation metrics are selected in order to assess the accuracy of the automated segmentation models in comparison with the ground truth data. Magnetic resonance images of 31 patients have been employed for the study. As result, U-Net shows better performance than the SegResNet in the automatic femur segmentation task.