Label2label: training a neural network to selectively restore cellular structures in fluorescence microscopy.

ABSTRACT: Immunofluorescence microscopy is routinely used to visualise the spatial distribution of proteins that dictates their cellular function. However, unspecific antibody binding often results in high cytosolic background signals, decreasing the image contrast of a target structure. Recently, convolutional neural networks (CNNs) were successfully employed for image restoration in immunofluorescence microscopy, but current methods cannot correct for those background signals. We report a new method that trains a CNN to reduce unspecific signals in immunofluorescence images; we name this method label2label (L2L). In L2L, a CNN is trained with image pairs of two non-identical labels that target the same cellular structure. We show that after L2L training a network predicts images with significantly

SUBMITTER: Kolln LS 

PROVIDER: S-EPMC8918818 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature