A Qualitative Comparison of Manual and K‐means Segmentation for Whole‐slide Histology Images of Rabbit Vocal Folds
Authors: Amsterdam Chloe , Philip Abel , Nirody Shawn , Sotelo Alexis , Huang Nasi , Hamilton James , Wisco Jonathan J. 
Published: May 2022

Introduction

Surgical treatment for subglottic stenosis often faces recurrence of narrowing and patients continue to face quality of life issues. Currently, biomechanical models that inform surgical treatments are difficult to create due to vocal fold intricacies and trouble distinguishing between tissue types–muscle, mucosa, and cartilage. Improved models of laryngeal microanatomy can be used to assess and predict surgical outcomes for patients who suffer from subglottic stenosis. While manual segmentation of histology is the standard, automatic segmentation can be more efficient, sensitive, and specific in tissue differentiation. Manual segmentation faces challenges like extensive operator time and inter‐rater subjectivity. The aim of this study, therefore, was to qualitatively compare manual segmentation to automatic k‐means segmentation of whole‐slide histology rabbit vocal fold images. This is the preliminary step towards enhancing segmentation and, later, achieving a high‐fidelity human larynx model.

Methods

134 whole‐slide histology images were manually segmented from seven rabbits using colored masks for muscle, cartilage, mucosa, and adipose tissue. Of those images, 31 were also selected for segmentation using k‐means for initial qualitative comparison. At least two images were segmented per rabbit. K‐means segmentation is optimal for our multidimensional data. Since it is deterministic, we selected the number of clusters as our parameter. After inspecting n=1‐10 clusters, n=4 was chosen as qualitatively producing the most distinct clusters without oversampling.

Results

For our preliminary results, we compared the histological image, the manual segmentation, and the automatically segmented image. In the automatic segmentations, tissue type is distinguished by the grayscale contrast. The four classes visible are cartilage, muscle, adipose tissue, and mucosa. There is occasional loss of visualization of adipose tissue and mucosa, however, the cartilage and muscle tissues remain distinct. The mucosal area boundaries remain clear despite some visualization loss within the mucosal borders, therefore, mucosa can be distinguished from adjacent cartilage and muscle. Training on a large dataset like other machine learning algorithms is unnecessary for k‐means, which is ideal for our relatively small sample size. The speed and versatility of k‐means has qualitatively demonstrated comparable results to our manual segmentations.

Conclusion

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.