Automated Nuclear Segmentation of Coherent Anti-Stokes Raman Scattering Microscopy Images by Coupling Superpixel Context Information with Artificial Neural Networks

Coherent anti-Stokes Raman scattering (CARS) microscopy is attracting major scientific attention because its high-resolution, label-free properties have great potential for real time cancer diagnosis during an image-guided-therapy process. In this study, we develop a nuclear segmentation technique which is essential for the automated analysis of CARS images in differential diagnosis of lung cancer subtypes. Thus far, no existing automated approaches could effectively segment CARS images due to their low signal-to-noise ratio (SNR) and uneven background. Naturally, manual delineation of cellular structures is time-consuming, subject to individual bias, and restricts the ability to process large datasets. Herein we propose a fully automated nuclear segmentation strategy by coupling superpixel context information and an artificial neural network (ANN), which is, to the best of our knowledge, the first automated nuclear segmentation approach for CARS images. The superpixel technique for local clustering divides an image into small patches by integrating the local intensity and position information. It can accurately separate nuclear pixels even when they possess subtly lower contrast with the background. The resulting patches either correspond to cell nuclei or background. To separate cell nuclei patches from background ones, we introduce the rayburst shape descriptors, and define a superpixel context index that combines information from a given superpixel and it's immediate neighbors, some of which are background superpixels with higher intensity. Finally we train an ANN to identify the nuclear superpixels from those corresponding to background. Experimental validation on three subtypes of lung cancers demonstrates that the proposed approach is fast, stable, and accurate for segmentation of CARS images, the first step in the clinical use of CARS for differential cancer analysis.