Microscopy Imaging Dataset: Trypanosoma brucei Bloodstream Form Classification Using Deep Learning

This dataset provides a comprehensive collection of microscopic images and associated labels, specifically designed to facilitate the automated classification of Trypanosoma brucei bloodstream forms—slender and stumpy. Accurate differentiation of these life cycle stages is vital for understanding the parasite's biology, transmission dynamics, and adaptation mechanisms in its mammalian host.

Contents:

• Image Data: Microscopic images of T. brucei bloodstream forms captured under standard imaging conditions, encompassing a broad array of image quality, cellular arrangements, and morphological characteristics.
• Label Data: Annotation files for each image, specifying cellular forms as slender or stumpy, essential for supervised machine learning applications.
• Supplementary Files: Additional Excel files providing information on training, testing, and validation splits, alongside test results for model evaluation.

Purpose:

This dataset serves as a valuable resource for researchers in parasitology, machine learning, and computational biology. It supports investigations into the biology and life cycle of T. brucei, while also providing a robust testbed for developing, validating, and benchmarking image processing and classification algorithms tailored to parasite morphology.

Data Collection and Methodology:

The dataset was compiled using advanced deep learning techniques, integrating the Cellpose segmentation algorithm with a custom-trained Xception model optimized for classifying T. brucei forms. The model achieved 97% classification accuracy, demonstrating effective application in handling complex cell images and distinguishing between slender and stumpy forms in challenging imaging conditions.

Usage:

Researchers are encouraged to use this dataset to:

• Analyze and classify the life cycle stages of T. brucei bloodstream forms in microscopic images.
• Develop and test deep learning models for single-cell image segmentation and classification.
• Explore cellular morphology patterns and refine machine learning approaches for other single-cell imaging applications.

Citation:

Please cite the original dataset if you utilize this resource in your research to acknowledge its contribution to the field.

Access and Availability:

This dataset is openly available through Zenodo, enabling researchers to download, explore, and apply it in various fields, from parasitology to advanced computational biology.