A Qualitative Comparison of Manual and K-means Segmentation for Whole-slide Histology Images of Rabbit Vocal Folds.

Future directions for our project include conducting k-means segmentation of the remainder of the 134 images collected. We have also tested mean-shift clustering and a deep learning convolutional neural network (CNN) to compare automatic segmentation methods to one another, and to the standard manual segmentation/ground truth. Future testing also includes quantitative analysis of these algorithms' accuracy. Beyond our histology images, we have at least 14 MR images (at least two per rabbit) of the larynges as well. We intend to combine and overlay the structural detail of the histology images with the soft tissue differentiation of the MRI.