# Recurrent U-net for automatic pelvic floor muscle segmentation on 3D ultrasound

arXiv: https://arxiv.org/abs/2107.13833 · HTML: https://mlsanity.com/paper/2107.13833/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 48 references that are in the map.
- First posted on arXiv 29 Jul 2021.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation](https://mlsanity.com/paper/1603.05959/) (2016)
- [Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation](https://mlsanity.com/paper/1802.06955/) (2018)
- [A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observations](https://mlsanity.com/paper/1406.2639/) (2014)
- [On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task](https://mlsanity.com/paper/1707.01992/) (2017)
- [Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation](https://mlsanity.com/paper/1609.01006/) (2016)
- [Hough-CNN: Deep Learning for Segmentation of Deep Brain Regions in MRI and Ultrasound](https://mlsanity.com/paper/1601.07014/) (2016)
- [Microscopy Cell Segmentation via Convolutional LSTM Networks](https://mlsanity.com/paper/1805.11247/) (2018)
- [A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation](https://mlsanity.com/paper/1806.02237/) (2018)
- [Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation](https://mlsanity.com/paper/1506.07452/) (2015)
- [MRI Tumor Segmentation with Densely Connected 3D CNN](https://mlsanity.com/paper/1802.02427/) (2018)
- [Joint Sequence Learning and Cross-Modality Convolution for 3D Biomedical Segmentation](https://mlsanity.com/paper/1704.07754/) (2017)
- [Recurrent Neural Networks for Semantic Instance Segmentation](https://mlsanity.com/paper/1712.00617/) (2017)
- [Improving Deep Pancreas Segmentation in CT and MRI Images via Recurrent Neural Contextual Learning and Direct Loss Function](https://mlsanity.com/paper/1707.04912/) (2017)
- [A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images](https://mlsanity.com/paper/1905.10835/) (2019)
- [Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalising neural network](https://mlsanity.com/paper/1712.06452/) (2017)
- [Random 2.5D U-net for Fully 3D Segmentation](https://mlsanity.com/paper/1910.10398/) (2019)

## Abstract (arXiv)

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.