Lymphocyte Classification in Hyperspectral Images of Ovarian Cancer Tissue Biopsy Samples

TL;DR

This paper introduces a machine learning pipeline for segmenting lymphocytes in hyperspectral FTIR images of ovarian cancer biopsies, aiming to improve diagnostic accuracy without traditional staining.

Contribution

It develops and evaluates a novel machine learning approach, including CNNs, for precise lymphocyte segmentation in hyperspectral images, addressing prior labeling challenges.

Findings

CNN outperforms traditional models in segmentation accuracy

Hyperspectral imaging enables stain-free lymphocyte detection

Machine learning improves diagnostic workflows for ovarian cancer

Abstract

Current methods for diagnosing the progression of multiple types of cancer within patients rely on interpreting stained needle biopsies. This process is time-consuming and susceptible to error throughout the paraffinization, Hematoxylin and Eosin (H&E) staining, deparaffinization, and annotation stages. Fourier Transform Infrared (FTIR) imaging has been shown to be a promising alternative to staining for appropriately annotating biopsy cores without the need for deparaffinization or H&E staining with the use of Fourier Transform Infrared (FTIR) images when combined with machine learning to interpret the dense spectral information. We present a machine learning pipeline to segment white blood cell (lymphocyte) pixels in hyperspectral images of biopsy cores. These cells are clinically important for diagnosis, but some prior work has struggled to incorporate them due to difficulty obtaining precise pixel labels. Evaluated methods include Support Vector Machine (SVM), Gaussian Naive Bayes, and Multilayer Perceptron (MLP), as well as analyzing the comparatively modern convolutional neural network (CNN).