Automated detection of wrist ganglia in MRI using convolutional neural networks

Abstract

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. 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. 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. 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.

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