CAMAlyzer: A 3DSlicer extension for AI segmentation and generation of proximal femur 3D models

—Femoroacetabular impingement syndrome (FAIS) is a condition that implies increased intra-articular forces due to abnormal morphology, leading to pain, reduction in the range of motion, and even early development of hip osteoarthritis [1]. Diagnosing FAIS is challenging, and there is a lack of tools that implement the state-of-the-art techniques based on the process of 3D modeling, considering that these are time-consuming, require a large amount of data, and user input [2]–[4].A 3D Slicer extension was developed, implementing a Machine learning pipeline that allows for generating a 3D reconstruction of the proximal femur in 16.2 ± 1.9 seconds. This reconstruction has an error close to other state-of-the-art techniques with a 95% Hausdorff distance of 4.8 ± 2.1 [mm]. This pipeline starts with an automatic Deep learning based segmentation using a 3D U-net architecture that was trained with a set of 3T Flash DIXON MRI images (27 images with 208 slices, 256x256 px, 0.7 mm thickness) of patients diagnosed with FAIS, which performed with 0.91 and 0.82 for the DICE index. This segmentation was later improved using morphological operations and the DBSCAN algorithm. This work allows us to obtain a 3D model that is faithful to the patient’s proximal femur, reducing segmentation time without losing the accuracy of manual segmentation. Finally, the extension it’s available for downloading and use at this link: https://github.com/VenjaminRodriguezR/CAMalyzer