ITK Analysis of Large Histology Datasets

Key Investigators

• OSU: Liya Ding, Kun Huang, Raghu Machiraju
• Harvard Medical School: Sean Megason

Project

Objective

3D histology stacks are being increasingly used to understand gross anatomical changes and to provide valuable educational contexts. Most existing toolkits allow a 2D approach and do not meet the challenges posed by 3D histology. ITKv4 can facilitate the realization application level toolkits that will allow for a sensible registration, segmentation and reconstruction of digital slides depicting various organs and tissue systems. Modules will be included that will allow for pre-processing (color correction, artifact removal, etc.), rigid and non-rigid registration, material-based segmentation, and visualization.

Approach, Plan

we have developed a series of algorithms and computational pipelines for processing large microscopy images using heterogeneous computing platforms including GPU and CPU/GPU clusters. We will extend ITKv4 by incorporating algorithm families that will allow for comprehensive processing of light microscopy images to enable both 2D and 3D digital histology.

Progress

We are creating three different workflows to achieve our goals. These workflows accomplish (i) pre-preprocessing the data, (ii) registration/3D reconstruction and segmentation/classification of tissue regions from multi-channel data, (iii) and the visualization of the microstructure.