Automated detection of wrist ganglia in MRI using convolutional neural networks.

BACKGROUND: To investigate feasibility of a method which combines segmenting convolutional neural networks (CNN) for the automated detection of ganglion cysts in 2D MRI of the wrist. The study serves as proof-of-concept, demonstrating a method to decrease false positives and offering an efficient solution for ganglia detection. METHODS: We retrospectively analyzed 58 MRI studies with wrist ganglia, each including 2D axial, sagittal, and coronal series. Manual segmentations were performed by a radiologist and used to train CNNs for automatic segmentation of each orthogonal series. Predictions were fused into a single 3D volume using a proposed prediction fusion method. Performance was evaluated over all studies using six-fold cross-validation, comparing method variations with metrics including true positive rate, number of false positives, and F-score metrics. RESULTS: The proposed method reached mean TPR of 0.57, mean FP of 0.4 and mean F-score of 0.53. Fusion of series predictions decreased the number of false positives significantly but also decreased TPR values. CONCLUSION: CNNs can detect ganglion cysts in wrist MRI. The number of false positives can be decreased by a method of prediction fusion from multiple CNNs.