Phase contrast images of bacteria and ground truth segmentations

<p><strong>Name</strong>: Phase contrast images of bacteria&nbsp;</p> <p><strong>Data type</strong>: Paired microscopy images and corresponding labels/masks used for model training, organized as recommended by the <a href="https://imagej.net/plugins/denoiseg">DenoiSeg documentation</a>.</p> <p><strong>Microscopy data type</strong>: Light microscopy (Phase Contrast)</p> <p><strong>Manual annotations</strong>: Labels/masks obtained via manual segmentation. For each region, all cells were annotated manually. Uncertain objects were left unannotated.</p> <p><strong>Microscope</strong>:&nbsp;Zeiss Axio Imager M2 epi-fluorescence microscope with a Zeiss Plan-Apochromat; 100x/1.4 oil DIC objective</p> <p><strong>File format</strong>: .tif (float 32-bits for phase contrast and 16-bit for mask images)</p> <p><strong>Image size</strong>: 256x256 pixels (Pixel size: 64.5 nm)</p> <p>&nbsp;</p> <p>Content:&nbsp;</p> <p>train - raw (33 files)&nbsp;</p> <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;- masks (33 files)</p> <p>test - raw (11 files)&nbsp;</p> <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;- masks (11 files)</p> <p>&nbsp;</p> <p>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:</p> <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> <p>&nbsp;</p>