Unsupervised convolutional autoencoders for 4D transperineal ultrasound classification

Purpose: 4D Transperineal ultrasound (TPUS) is used to examine female pelvic floor disorders. Muscle movement, like performing a muscle contraction or a Valsalva maneuver, can be captured on TPUS. Our work investigates the possibility for unsupervised analysis and classification of the TPUS data. Approach: An unsupervised 3D-convolutional autoencoder is trained to compress TPUS volume frames into a latent feature vector (LFV) of 128 elements. The (co)variance of the features are analyzed and statistical tests are performed to analyze how features contribute in storing contraction and Valsalva information. Further dimensionality reduction is applied (principal component analysis or a 2D-convolutional autoencoder) to the LFVs of the frames of the TPUS movie to compress the data and analyze the interframe movement. Clustering algorithms (K-means clustering and Gaussian mixture models) are applied to this representation of the data to investigate the possibilities of unsupervised classification. Results: The majority of the features show a significant difference between contraction and Valsalva. The (co)variance of the features from the LFVs was investigated and features most prominent in capturing muscle movement were identified. Furthermore, the first principal component of the frames from a single TPUS movie can be used to identify movement between the frames. The best classification results were obtained after applying principal component analysis and Gaussian mixture models to the LFVs of the TPUS movies, yielding a 91.2% accuracy. Conclusion: Unsupervised analysis and classification of TPUS data yields relevant information about the type and amount of muscle movement present.