Center for Bioimage Informatics

The Center for Bioimage Informatics (CBI) provides expertise and resources to enable, accelerate, and enhance image-based research at St. Jude. Bioimage informatics integrates advanced artificial intelligence (AI), machine learning and computational methods to extract quantitative insights from bioimaging data, maximizing the impact of rapidly advancing imaging technologies. As imaging modalities grow increasingly sophisticated, our team delivers innovative image processing solutions. These include state-of-the-art image restoration, multimodal registration, precise segmentation, robust visualization and the development of deep learning-driven analytical tools.

The Center for Bioimage Informatics empowers imaging-based research across diverse modalities at St. Jude, including advanced light microscopy, electron microscopy and magnetic resonance imaging. With enterprise-scale GPU-equipped workstations and high-performance computing nodes, the CBI provides robust, scalable and efficient data processing. User-friendly web portals give researchers seamless access to self-service analytics on the institution’s computing infrastructure.

The multidisciplinary CBI team brings expertise in machine learning, deep learning, computer vision, quantitative image analysis and interactive data visualization. Advanced image data management, combined with custom-developed, open-source and commercial image processing software, strengthens these efforts.

CBI’s impact extends through active collaborations with research labs and departments across St. Jude. We also maintain close partnerships with microscopy and imaging facilities, ensuring seamless integration from data acquisition to analysis. These efforts contribute steadily to peer-reviewed research. Equally important is our commitment to the broader bioimage community: releasing open-source software packages, tools and resources that make advanced analysis methods widely accessible. These efforts ensure investigators benefit from innovative AI-driven bioimage analysis while fostering shared progress across the field.