CryoET Object Identification
Rank: 156th out of 833
Annotating protein complexes in 3D cellular images to accelerate biomedical discoveries and disease treatment
About the competition
Protein complexes are essential for cell function, and understanding their interactions can advance health and disease treatments. Cryo-electron tomography (cryoET) generates 3D images of proteins in their natural environments, at near-atomic detail, offering detailed insights into cellular function.
However, standardized cryoET tomograms remain underexplored due to the difficulty of automating protein identification. Manual annotation is time-consuming and limited by human capabilities, but automating this process could reveal cellular "dark matter", leading to discoveries to improve human health.
This competition aims to train an AI model to automatically annotate five types of protein complexes using a real-world cryoET dataset, accelerating discoveries and unlocking the mysteries of the cell.
CZ Imaging Institute
The competition is organized by the “Chan Zuckerberg Imaging Institute”. They develop advanced imaging technologies, enabling new insights into health and disease. By creating cutting-edge tools and biological probes, it empowers researchers to visualize cellular structures with unmatched resolution.
Focused on collaboration, the institute drives innovation through open-source tools, groundbreaking research, and accessible imaging systems, advancing biology and biomedicine.
Relevance
Understanding protein complexes is crucial for addressing global health challenges. Diseases like cancer, neurodegenerative disorders, and infections are rooted in the interactions of these cellular components. CryoET imaging provides new insights, but without automated annotation tools, these images remain largely underexplored.
This competition directly supports advancements in healthcare by developing AI models that make cryoET data more accessible and interpretable. These tools will empower scientists to identify molecular targets, design effective treatments, and improve patient outcomes on a global scale.
Technical Details
The dataset for this competition consists of a cube containing 3D protein samples. The goal is to develop a multiclass localization model capable of identifying the center of any protein sample present in the data.
One of the key challenges lies in the extremely low signal-to-noise ratio (SNR). This is due to the nature of electron tomography, the technique used to sample the proteins. During this process, a high density of electrons cannot be used, as it would damage the delicate protein samples. Navigating around the low SNR while maintaining accuracy remains a significant hurdle.
The team is currently focused on researching and implementing segmentation models, which are later converted into localization coordinates during post-processing. Both YOLO and U-Net models have proven to be the most effective for this use case.
UN Sustainable Development Goals
This competition supports UN Sustainable Development Goal #3: Good Health and Well-Being. The AI model aims to enhance understanding of cellular processes, enabling earlier and more accurate diagnostics and advancing biomedical science.
