MotilitAI

With motilitAI, human semen samples from the VISEM dataset can be automatically assessed 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 videos are adopted for unsupervised tracking and two different feature extraction methods, in particular custom movement statistics (cms) and mean squared displacement features (msd). We train Support Vector Regressors (SVR), Multilayer Perceptron (MLP) models, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) on the extracted features. Best results are achieved using a linear SVR on an aggregated and quantised representation of individual displacement features of each sperm cell.

Citing

If you use motilitAI in your research work, you are kindly asked to acknowledge the use of motilitAI in your publications.

S. Ottl, S. Amiriparian, M. Gerczuk and B. Schuller, "A Machine Learning Framework for Automatic Prediction of Human Semen Motility", 2021. https://arxiv.org/abs/2109.08049

@article{ottl2021machine,
  title={A Machine Learning Framework for Automatic Prediction of Human Semen Motility},
  author={Ottl, Sandra and Amiriparian, Shahin and Gerczuk, Maurice and Schuller, Björn},
  journal={arXiv preprint arXiv:2109.08049},
  year={2021}
}

Installation

Create and activate a conda environment from the provided environment.yml file:

conda env create --name motilitAI --file=environment.yml
conda activate motilitAI

Usage

Feature extraction

Put the extracted visem dataset into ./data/raw such that you have a folder ./data/raw/videos which contains all of the original videosamples.

Then, you can run dvc repro to preprocess the videos, extract displacement features and apply XBoW quantisation to them.

Prediction Models

There are two Machine Learning approaches, one using ensemble mean squared displacement (emsd) features to train different models, and one with Bags-of-Words (BoWs) created from either custom movement statistics (cms) or individual mean squared displacement (imsd) features.

python models/[emsd|bow]-prediction-models.py <features_fold_1> <features_fold_2> <features_fold_3> <additional options>

Shared CLI options

Variable	Type	Description	Required	Default value
-md, --model-dir	string	Model directory.	false	model
-ri, --random-iterations	int	Number of random grid serach iterations.	false	10

Specific CLI options for motility prediction with emsd features

Variable	Type	Description	Required	Default value
-ma, --model-architecture	choices=[svr, mlp, cnn, rnn]	Type of neural network architecture.	false	cnn
-sd, --semen-data	string	Path to semen_data_analysis csv file.	false	sement_data_analysis.csv

Specific CLI options for motility prediction with BoWs

Variable	Type	Description	Required	Default value
-bd, --bow-dirname	string	BoW directory name.	true
-ma, --model-architecture	choices=[svr, mlp]	Type of neural network architecture.	false	mlp
-s, --sizes	int	Sizes for BoW.	1+	[2500, 5000]
-a, --assignment-vectors	int	Number of assigned codebook vectors.	1+	[50]
-sn, --session-number	int	Starting session.	false	0