Fig. 2: Image-segmentation networks (U-Net and StarDist).
From: Democratising deep learning for microscopy with ZeroCostDL4Mic
a, b Example of data generated using the ZeroCostDL4Mic U-Net and StarDist notebooks. a A 2D U-Net model was trained to segment neuronal membranes from EM images. This training dataset is from the 2012 ISBI segmentation challenge78. Training source (raw data), training targets (hand-annotated binary masks), predictions (raw output of the notebook after training) and U-Net image thresholded output are displayed, achieving an Intersection over Union (IoU) of 0.90 (see Supplementary Note 2 for details). The optimal threshold was assessed automatically using the Quality Control section of the notebook (see Supplementary Note 3). b A 3D U-Net network was trained to segment mitochondria from EM images. The training dataset was made available by EPFL and consists of EM images of 5 × 5 × 5 µm3 sections taken from the CA1 hippocampus region of the brain. A representative single Z slice, as well as an overlay displaying U-Net prediction and the ground truth, are displayed. 3D reconstructions displayed were performed from U-Net predictions using Imaris (Supplementary Movie 3). c, d Example of data generated using the ZeroCostDL4Mic StarDist notebooks. c, d A StarDist model was trained (c) to automatically detect nuclei in movies of migrating DCIS.COM cells, labelled with SiR-DNA, to track their movement automatically (d). c Example of Training source (DCIS.COM cells labelled with SiR-DNA), Training targets (Ground-truth masks) and StarDist prediction (IoU of 0.86) are displayed. d StarDist outputs were used to automatically track cell movement over time in TrackMate (Supplementary Movie 4). Cell tracks were further analysed using the online platform motilitylab.net, indicating a directed movement that is expected for such migration assays (error bars represent the standard deviation). IoU Intersection over Union.
