# Separating Overlapping Tissue Layers from Microscopy Images

## TL;DR

This paper introduces a novel imaging model and algorithm that effectively separate overlapping tissue layers in microscopy images, enabling better digital processing of tissue slices with tears and overlaps.

## Contribution

The paper presents a new model and algorithm specifically designed to digitally separate overlapping tissue layers in microscopy images, addressing a key challenge in tissue image analysis.

## Key findings

- The model accurately separates tissue layers in mouse brain images.
- The algorithm's results closely match ground truth data.
- It enables processing of images previously discarded due to overlaps.

## Abstract

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.