Cancer cytoplasm segmentation in hyperspectral cell image with data augmentation
This work addresses the challenge of accurate cancer cytoplasm segmentation for medical diagnosis, but it is incremental as it focuses on data augmentation rather than a novel segmentation method.
The paper tackles the problem of segmenting cancer cytoplasm in hyperspectral cell images by proposing a data augmentation method using CMOS images to address instrumental noise and dataset scarcity, showing effectiveness quantitatively and qualitatively.
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.