Phase contrast images of bacteria and ground truth segmentations

<strong>Name</strong>: Phase contrast images of bacteria <strong>Data type</strong>: Paired microscopy images and corresponding labels/masks used for model training, organized as recommended by the DenoiSeg documentation. <strong>Microscopy data type</strong>: Light microscopy (Phase Contrast) <strong>Manual annotations</strong>: Labels/masks obtained via manual segmentation. For each region, all cells were annotated manually. Uncertain objects were left unannotated. <strong>Microscope</strong>: Zeiss Axio Imager M2 epi-fluorescence microscope with a Zeiss Plan-Apochromat; 100x/1.4 oil DIC objective <strong>File format</strong>: .tif (float 32-bits for phase contrast and 16-bit for mask images) <strong>Image size</strong>: 256x256 pixels (Pixel size: 64.5 nm) Content: train - raw (33 files) - masks (33 files) test - raw (11 files) - masks (11 files) All images available in the raw folders were normalized by dividing the original images with a gaussian blurred version or the original image (200 pixels). A groovy code working within ImageJ/Fiji corresponding to this operation is as follow: <pre><code class="language-java">ImagePlus normalize(ImagePlus input_image) { flatfield = (new Duplicator()).run(input_image) (new GaussianBlur()).blur(flatfield.getProcessor(), 200) return ImageCalculator.run(input_image, flatfield, "Divide create 32-bit") } import ij.ImagePlus import ij.plugin.Duplicator import ij.plugin.ImageCalculator import ij.plugin.filter.GaussianBlur</code></pre>