MVFStain: Multiple virtual functional stain histopathology images generation based on specific domain mapping.
Journal:
Medical image analysis
Published Date:
Jun 23, 2022
Abstract
To the best of our knowledge, artificial intelligence stain generation is an urgent requirement for histopathology images. Pathological examinations usually only utilize hematoxylin and eosin (H&E) regular staining to show histomorphological characteristics, but to accurately diagnose the disease, functional staining (such as oil red O and Ki67) are also required to provide important auxiliary information. However, the same tissue section is usually stained with one stain, and additional functional staining is not only time-consuming but also causes inevitable morphological misalignment due to manual manipulation. This brings difficulties to the development of artificial intelligence pathological image analysis tools. In this work, we propose a histopathology staining transfer framework to generate virtual functional staining images from H&E regular staining images. Compared with the framework that emphasizes generation diversity in the natural image field, we use KL loss and histo loss to align and separate style feature spaces in different domains to obtain domain-variant style features. The proposed multiple virtual functional stain (MVFStain) abstracts staining conversion to domain mapping and comprehensively utilizes multiple staining information. We evaluated the proposed method on four datasets (lung lesion, lung lobes, breast, and atherosclerotic lesion). The experiment involves the translation of H&E to nine other functional stains: CC10, Ki67, proSPC, HER2, PR, ER, oil red O, α-SMA, and macrophages. The major quantitative results are divided into image quality and positive signal prediction. MVFStain is close to or even surpasses one-to-one image translation on psnr and HTI image quality metrics. The best psnr reaches 26.1919, and HIT reaches 0.9430. We used mIOD to evaluate the optical density of positive signals, and CNR and gCNR to evaluate the lesion detectability. The results show that the mIOD of positive signals of virtual staining was slightly lower than the ground truth and close the lesion detectability of artificial staining. These results prove that the potential exists to develop a successful clinical alternative to artificial functional stains.