This study aimed to develop a deep learning-based approach to automatically segment the femoral articular cartilage (FAC) in 3D ultrasound (US) images of the knee to increase time efficiency and decrease rater variability. Our method involved deep learning predictions on 2DUS slices sampled in the transverse plane to view the cartilage of the femoral trochlea, followed by reconstruction into a 3D surface. A 2D U-Net was modified and trained using a dataset of 200 2DUS images resliced from 20 3DUS images. Segmentation accuracy was evaluated using a holdout dataset of 50 2DUS images resliced from 5 3DUS images. Absolute and signed error metrics were computed and FAC segmentation performance was compared between rater 1 and 2 manual segmentations. Our U-Net-based algorithm performed with mean 3D DSC, recall, precision, VPD, MSD, and HD of 73.1 ​± ​3.9%, 74.8 ​± ​6.1%, 72.0 ​± ​6.3%, 10.4 ​± ​6.0%, 0.3 ​± ​0.1 ​mm, and 1.6 ​± ​0.7 ​mm, respectively. Compared to the individual 2D predictions, our algorithm demonstrated a decrease in performance after 3D reconstruction, but these differences were not found to be statistically significant. The percent difference between the manually segmented volumes of the 2 raters was 3.4%, and rater 2 demonstrated the largest VPD with 14.2 ​± ​11.4 ​mm3 compared to 10.4 ​± ​6.0 ​mm3 for rater 1. This study investigated the use of a modified U-Net algorithm to automatically segment the FAC in 3DUS knee images of healthy volunteers, demonstrating that this segmentation method would increase the efficiency of anterior femoral cartilage volume estimation and expedite the post-acquisition processing for 3D US images of the knee.