Cancer cytoplasm segmentation in hyperspectral cell image with data augmentation
Journal:
arXiv
Published Date:
Jul 4, 2025
Abstract
Hematoxylin and Eosin (H&E)-stained images are commonly used to detect
nuclear or cancerous regions in cells from images captured by a microscope.
Identifying cancer cytoplasm is crucial for determining the type of cancer;
hence, obtaining accurate cancer cytoplasm regions in cell images is important.
While CMOS images often lack detailed information necessary for diagnosis,
hyperspectral images provide more comprehensive cell information. Using a deep
learning model, we propose a method for detecting cancer cell cytoplasm in
hyperspectral images. Deep learning models require large datasets for learning;
however, capturing a large number of hyperspectral images is difficult.
Additionally, hyperspectral images frequently contain instrumental noise,
depending on the characteristics of the imaging devices. We propose a data
augmentation method to account for instrumental noise. CMOS images were used
for data augmentation owing to their visual clarity, which facilitates manual
annotation compared to original hyperspectral images. Experimental results
demonstrate the effectiveness of the proposed data augmentation method both
quantitatively and qualitatively.
