Recurrent U-net for automatic pelvic floor muscle segmentation on 3D ultrasound
This incremental work addresses pelvic floor problems in the female population by enabling automated analysis for large study populations.
The study tackled automating 3D segmentation of the levator ani muscle in transperineal ultrasound volumes using a U-net with convolutional LSTM layers, achieving human-level performance.
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.