A Probabilistic Model Combining Deep Learning and Multi-atlas Segmentation for Semi-automated Labelling of Histology

Thanks to their high resolution and contrast enhanced by different stains, histological images are becoming increasingly widespread in atlas construction. Building atlases with histology requires manual delineation of a set of regions of interest on a large amount of sections. This process is tedious, time-consuming, and rather inefficient due to the high similarity of adjacent sections. Here we propose a probabilistic model for semi-automated segmentation of stacks of histological sections, in which the user manually labels a sparse set of sections (e.g., one every n), and lets the algorithm complete the segmentation for other sections automatically. The proposed model integrates in a principled manner two families of segmentation techniques that have been very successful in brain imaging: multi-atlas segmentation (MAS) and convolutional neural networks (CNNs). Within this model, we derive a Generalised Expectation Maximisation algorithm to compute the most likely segmentation. Experiments on the Allen dataset show that the model successfully combines the strengths of both techniques (effective label propagation of MAS, and robustness to misregistration of CNNs), and produces significantly more accurate results than using either of them independently.