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.
To create conditional atlases that also align images, the researchers combined two neural networks: One network automatically learns an atlas at each iteration, and another — adapted from the previous research — simultaneously aligns that atlas to images in a dataset.
In training, the joint network is fed a random image from a dataset encoded with desired patient attributes. From that, it estimates an attribute-conditional atlas. The second network aligns the estimated atlas with the input image and generates a deformation field.