Elastic and Collagen Fibers Segmentation Based on U-Net Deep Learning Using Hematoxylin and Eosin Stained Hyperspectral Images

Abstract

In Hematoxylin and Eosin (H&E) stained images, it is difficult to distinguish collagen and elastic fibers because these are similar in color and texture. This study tries to segment the appearance of elastic and collagen fibers based on U-net deep learning using spatial and spectral information of H&E stained hyperspectral images. Groundtruth of the segmentation is obtained using Verhoeff’s Van Gieson (EVG) stained images, which are commonly used for recognizing elastic and collagen fiber regions. Our model is evaluated by three cross-validations. The segmentation results show that the combination of spatial and spectral features in H&E stained hyperspectral images performed better segmentation than H&E stained in conventional RGB images compare to the segmentation of EVG stained images as ground truth by visually and quantitatively.