MIT researchers have devised a method that accelerates the process for creating and customizing templates used in medical-image analysis, to guide disease diagnosis.
One use of medical image analysis is to crunch datasets of patients’ medical images and capture structural relationships that may indicate the progression of diseases. In many cases, analysis requires use of a common image template, called an “atlas,” that’s an average representation of a given patient population. Atlases serve as a reference for comparison, for example to identify clinically significant changes in brain structures over time.
Building a template is a time-consuming, laborious process, often taking days or weeks to generate, especially when using 3D brain scans. To save time, researchers often download publicly available atlases previously generated by research groups. But those don’t fully capture the diversity of individual datasets or specific subpopulations, such as those with new diseases or from young children. Ultimately, the atlas can’t be smoothly mapped onto outlier images, producing poor results.
In a paper being presented at the Conference on Neural Information Processing Systems in December, the researchers describe an automated machine-learning model that generates “conditional” atlases based on specific patient attributes, such as age, sex, and disease. By leveraging shared information from across an entire dataset, the model can also synthesize atlases from patient subpopulations that may be completely missing in the dataset.