Segmentation of hyphae and yeast in fungi-infected tissue slice images and its application in analyzing anti-fungal blue light therapy.

Abstract

Candida albicans (C. albicans) is a pathogenic fungus that undergoes morphological transitions between hyphal and yeast, adapting to diverse environmental stimuli and exhibiting distinct virulence. Existing researches on anti-fungal blue light (ABL) therapy have either focused solely on hyphae or neglected to differentiate between morphologies, obscuring potential differential effects. To address this gap, we established a novel dataset of 150 C. albicans-infected mouse skin tissue slice images with meticulously annotated hyphae and yeast. Eleven representative convolutional neural networks were trained and evaluated on this dataset using seven metrics to identify the optimal model for segmenting hyphae and yeast in original high pixel size images. Leveraging the segmentation results, we analyzed the differential impact of blue light on the invasion depth and density of both morphologies within the skin tissue. Comparison results demonstrated that U-Net-BN emerged as superior segmentation accuracy compared to other models, achieving the best overall performance. While both hyphae and yeast exhibited significant reductions in invasion depth and density at the highest ABL dose (180J/cm2), only yeast was significantly inhibited at the lower dose (135J/cm2). This novel finding emphasizes the importance of developing more effective treatment strategies for both morphologies.