  The prevalance of pelvic floor problems is high within the female population.
Transperineal ultrasound (TPUS) is the main imaging modality used to
investigate these problems. Automating the analysis of TPUS data will help in
growing our understanding of pelvic floor related problems. In this study we
present a U-net like neural network with some convolutional long short term
memory (CLSTM) layers to automate the 3D segmentation of the levator ani muscle
(LAM) in TPUS volumes. The CLSTM layers are added to preserve the inter-slice
3D information. We reach human level performance on this segmentation task.
Therefore, we conclude that we successfully automated the segmentation of the
LAM on 3D TPUS data. This paves the way towards automatic in-vivo analysis of
the LAM mechanics in the context of large study populations.
