Lymphoma Cell Nuclei Classification using Color and Morphology Features

Abstract

Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of Non-Hodgkin’s Lymphoma, presenting a great challenge for treatment due to its highly heterogeneous nature. DLBCL is diagnosed based on microscopy images of patient tissue samples. To help gain a better understanding of DLBCL, we developed an automated computer vision method to analyze morphological and color-based information within patient biopsies. We analyzed a dataset of whole slide images of DLBCL by segmenting individual cells and representing cell morphologies through a set of engineered features. The features were evaluated using a variety of visualization and machine learning (ML) classification techniques. Current state-of-the-art deep learning methods use images as the input in classification tasks achieving high performance but lacking in interpretability. A big challenge lies in finding out what features the pixel-based deep learning methods utilize in prediction. Here, we present a technique that not only yields high prediction accuracy but also provides insights into which of the features are key for prediction. We show that the color-based features have the highest importance for cell classification, allowing for the accurate identification of various cell types with an accuracy of 84% in a multi-class and 91% in a binary classification. Our results provide valuable insights for exploring cell image datasets to gain an in-depth view of the tumor microenvironment.

Keywords

• nuclei classification
• machine learning
• feature engineering