microglia_analyzer package

Subpackages

• microglia_analyzer.tiles package
  • Submodules
    • microglia_analyzer.tiles.patch module
      • Patch2D
    • microglia_analyzer.tiles.plot module
      • plot_patches_layout()
    • microglia_analyzer.tiles.recalibrate module
      • get_net2ori_factor()
      • get_ori2net_factor()
      • process_factor()
      • recalibrate_shape()
      • scaling
    • microglia_analyzer.tiles.tiler module
      • ImageTiler2D
      • make_gradient_patch()
      • normalize()
  • Module contents

Submodules

• microglia_analyzer.custom_loss module
  • compute_connected_components_penalty()
  • compute_skeleton_reward()
  • custom_loss()
  • dice_loss_function()
• microglia_analyzer.ma_worker module
  • MicrogliaAnalyzer
    • MicrogliaAnalyzer.analyze_graph()
    • MicrogliaAnalyzer.as_tsv()
    • MicrogliaAnalyzer.bindings_from_yolo()
    • MicrogliaAnalyzer.bindings_to_yolo()
    • MicrogliaAnalyzer.classify_microglia()
    • MicrogliaAnalyzer.get_classification_version()
    • MicrogliaAnalyzer.get_mask()
    • MicrogliaAnalyzer.get_model_path()
    • MicrogliaAnalyzer.get_segmentation_version()
    • MicrogliaAnalyzer.reset_classification()
    • MicrogliaAnalyzer.reset_segmentation()
    • MicrogliaAnalyzer.segment_microglia()
    • MicrogliaAnalyzer.set_calibration()
    • MicrogliaAnalyzer.set_classification_model()
    • MicrogliaAnalyzer.set_input_image()
    • MicrogliaAnalyzer.set_min_surface()
    • MicrogliaAnalyzer.set_proba_threshold()
    • MicrogliaAnalyzer.set_segmentation_model()
    • MicrogliaAnalyzer.set_working_directory()
• microglia_analyzer.qt_workers module
  • QtBatchRunners
    • QtBatchRunners.finished
    • QtBatchRunners.interupt()
    • QtBatchRunners.make_mosaic()
    • QtBatchRunners.run()
    • QtBatchRunners.to_kill
    • QtBatchRunners.update
    • QtBatchRunners.workflow()
    • QtBatchRunners.write_classification()
    • QtBatchRunners.write_csv()
    • QtBatchRunners.write_mask()
    • QtBatchRunners.write_skeleton()
    • QtBatchRunners.write_tsv()
    • QtBatchRunners.write_visual_check()
  • QtClassifyMicroglia
  • QtMeasureMicroglia
    • QtMeasureMicroglia.finished
    • QtMeasureMicroglia.run()
    • QtMeasureMicroglia.update
  • QtSegmentMicroglia
  • QtVersionableDL
    • QtVersionableDL.finished
    • QtVersionableDL.run()
    • QtVersionableDL.update
• microglia_analyzer.rename-files module
• microglia_analyzer.unet_worker module
  • UNetWorker
    • UNetWorker.inference()
• microglia_analyzer.utils module
  • best_grid_shape()
  • bindings_as_napari_shapes()
  • calculate_iou()
  • convert_hex_colors_to_bgr()
  • create_titled_canvas()
  • download_from_web()
  • draw_bounding_boxes()
  • generate_random_bindings()
  • get_all_tiff_files()
  • hex_to_bgr()
  • normalize_batch()
  • save_as_fake_colors()
  • write_mosaic()

Module contents

microglia_analyzer.TIFF_REGEX = re.compile('(.+)\\.tiff?', re.IGNORECASE)

The networks used in this package (A 2D UNet and a YOLOv5) rely on images that have a pixel size of 0.325 µm. Images with a different pixel size will be resized to artificially have a pixel size of 0.325 µm. It is from these resized images that we will extract the patches.