Separating Overlapping Tissue Layers from Microscopy Images
This addresses a specific issue in microscopy imaging for biomedical research, but it is incremental as it builds on existing digital processing methods.
The paper tackles the problem of separating overlapping tissue layers in microscopy images, which are typically discarded due to artifacts, by proposing an imaging model and algorithm that successfully separates them into two layers, as validated by ground truth comparisons.
Manual preparation of tissue slices for microscopy imaging can introduce tissue tears and overlaps. Typically, further digital processing algorithms such as registration and 3D reconstruction from tissue image stacks cannot handle images with tissue tear/overlap artifacts, and so such images are usually discarded. In this paper, we propose an imaging model and an algorithm to digitally separate overlapping tissue data of mouse brain images into two layers. We show the correctness of our model and the algorithm by comparing our results with the ground truth.