"Mommy, how will the baby get out of your tummy? Will it hurt you?"

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.

Appearance of the levator ani muscle subdivisions on 3D transperineal ultrasound

BACKGROUND

The levator ani muscle (LAM) consists of different subdivisions, which play a specific role in the pelvic floor mechanics. The aim of this study is to identify and describe the appearance of these subdivisions on 3-Dimensional (3D) transperineal ultrasound (TPUS). To do so, a study designed in three phases was performed in which twenty 3D TPUS scans of vaginally nulliparous women were assessed. The first phase was aimed at getting acquainted with the anatomy of the LAM subdivisions and its appearance on TPUS: relevant literature was consulted, and the TPUS scan of one patient was analyzed to identify the puborectal, iliococcygeal, puboperineal, pubovaginal, and puboanal muscle. In the second phase, the five LAM subdivisions and the pubic bone and external sphincter, used as reference structures, were manually segmented in volume data obtained from five nulliparous women at rest. In the third phase, intra- and inter-observer reproducibility were assessed on twenty TPUS scans by measuring the Dice Similarity Index (DSI).

RESULTS

The mean inter-observer and median intra-observer DSI values (with interquartile range) were: puborectal 0.83 (0.13)/0.83 (0.10), puboanal 0.70 (0.16)/0.79 (0.09), iliococcygeal 0.73 (0.14)/0.79 (0.10), puboperineal 0.63 (0.25)/0.75 (0.22), pubovaginal muscle 0.62 (0.22)/0.71 (0.16), and the external sphincter 0.81 (0.12)/0.89 (0.03).

CONCLUSION

Our results show that the LAM subdivisions of nulliparous women can be reproducibly identified on 3D TPUS data.