One-shot learning of anatomical structure localization models

We propose an approach which allows to localize anatomical landmarks in radiological datasets given only a single manual annotation and set of un-annotated example images. Using top-down image patch regression to obtain potential landmark candidates in the set of training images, a model of the anatomical structure is incrementally enlarged, starting from the single, annotated image, until it encompasses the entire training set. The obtained model then allows to perform highly accurate anatomical structure localization on test data. We report preliminary results on a set of 2D radio-graphs, with a median/mean localization residual of 0.92 mm/1.30 mm. The approach yields very promising localization results, suggesting that is possible to eliminate the tedious manual annotation process still required by state of the art localization approaches.