  In this paper, human semen samples from the visem dataset collected by the
Simula Research Laboratory are automatically assessed with machine learning
methods for their quality in respect to sperm motility. Several regression
models are trained to automatically predict the percentage (0 to 100) of
progressive, non-progressive, and immotile spermatozoa in a given sample. The
video samples are adopted for three different feature extraction methods, in
particular custom movement statistics, displacement features, and motility
specific statistics have been utilised. Furthermore, four machine learning
models, including linear Support Vector Regressor (SVR), Multilayer Perceptron
(MLP), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN),
have been trained on the extracted features for the task of automatic motility
prediction. Best results for predicting motility are achieved by using the
Crocker-Grier algorithm to track sperm cells in an unsupervised way and
extracting individual mean squared displacement features for each detected
track. These features are then aggregated into a histogram representation
applying a Bag-of-Words approach. Finally, a linear SVR is trained on this
feature representation. Compared to the best submission of the Medico
Multimedia for Medicine challenge, which used the same dataset and splits, the
Mean Absolute Error (MAE) could be reduced from 8.83 to 7.31. For the sake of
reproducibility, we provide the source code for our experiments on GitHub.
