intro.ipynb

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   "source": [
    "# Ultrack I2K 2023 - Introduction\n",
    "\n",
    "This tutorial will introduce the basic concepts of Ultrack and how to use it to track cells when segmentation is already available.\n",
    "\n",
    "## Setting up Colab runtime\n",
    "\n",
    "If you are using Colab, we recommend to set up the runtime to use a GPU.\n",
    "To do so, go to `Runtime > Change runtime type` and select `GPU` as the hardware accelerator.\n",
    "\n",
    "## Setup Dependencies\n",
    "\n",
    "This step is only necessary if you are on Colab or don't have the required packages.\n",
    "\n",
    "IMPORTANT: The runtime must be initialized.\n",
    "\n",
    "Uncomment and run the following commands to install all required packages."
   ]
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   "source": [
    "# !pip install stackview cellpose 'napari[all]' ultrack ipycanvas==0.11 cucim \n",
    "# !pip install git+https://github.com/Janelia-Trackathon-2023/traccuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "809e9911-2b8d-49ff-8b82-f74a27749697",
   "metadata": {},
   "source": [
    "## Download Dataset\n",
    "\n",
    "Download the Fluo-C2DL-Huh7 dataset from the [Cell Tracking Challenge](celltrackingchallenge.net), which contains fluorescence microscopy images for cell tracking.\n",
    "\n",
    "The dataset will be used for demonstrating the segmentation and tracking workflow."
   ]
  },
  {
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    "id": "017c2766-fc33-42f9-a0f2-30fda422c59f",
    "outputId": "dd5f1b72-7364-4786-c38f-04710a11e1e9"
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   "outputs": [],
   "source": [
    "!wget -nc http://data.celltrackingchallenge.net/training-datasets/Fluo-C2DL-Huh7.zip\n",
    "!unzip -n Fluo-C2DL-Huh7.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e172a2f-cc07-467d-bf50-0453019dbff2",
   "metadata": {},
   "source": [
    "## Import Libraries\n",
    "\n",
    "Import the libraries needed for reading images, processing them, cell segmentation, tracking, and performance metrics. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "923c68b7",
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    "id": "923c68b7"
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   "source": [
    "from pathlib import Path\n",
    "from cellpose.models import Cellpose\n",
    "\n",
    "import numpy as np\n",
    "import stackview\n",
    "from dask.array.image import imread\n",
    "from numpy.typing import ArrayLike\n",
    "from rich import print\n",
    "\n",
    "from traccuracy import run_metrics\n",
    "from traccuracy.loaders import load_ctc_data\n",
    "from traccuracy.matchers import CTCMatched\n",
    "from traccuracy.metrics import CTCMetrics\n",
    "\n",
    "from ultrack import track, to_tracks_layer, tracks_to_zarr, to_ctc\n",
    "from ultrack.utils import labels_to_edges\n",
    "from ultrack.config import MainConfig\n",
    "from ultrack.imgproc import normalize\n",
    "from ultrack.imgproc.segmentation import Cellpose\n",
    "from ultrack.utils.array import array_apply"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39fcb0a2-458c-4301-a372-e71e8893ab71",
   "metadata": {},
   "source": [
    "## Colab or Local\n",
    "\n",
    "Change the `COLAB` variable to `True` or `False` depending on whether you are running this notebook on Colab or locally.\n",
    "\n",
    "When running locally napari will be used a the image viewer, while on Colab the images will be displayed using `stackview`."
   ]
  },
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   "source": [
    "COLAB = True\n",
    "# COLAB = False\n",
    "\n",
    "if COLAB:\n",
    "    viewer = None\n",
    "\n",
    "    # fixes colab encoding error\n",
    "    import locale\n",
    "    locale.getpreferredencoding = lambda: \"UTF-8\"\n",
    "\n",
    "    # enabling colab output\n",
    "    try:\n",
    "        from google.colab import output\n",
    "        output.enable_custom_widget_manager()\n",
    "    except ModuleNotFoundError as e:\n",
    "        print(e)\n",
    "else:\n",
    "    import napari\n",
    "    from napari.utils import nbscreenshot\n",
    "\n",
    "    viewer = napari.Viewer()\n",
    "\n",
    "    def screenshot() -> None:\n",
    "        display(nbscreenshot(viewer))"
   ]
  },
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   "cell_type": "markdown",
   "id": "e8f8867a-ce1f-4860-a8f2-8a68497ae403",
   "metadata": {},
   "source": [
    "## Load Data\n",
    "\n",
    "Load the Fluo-C2DL-Huh7 dataset."
   ]
  },
  {
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   "source": [
    "dataset = \"02\"\n",
    "path = Path(\"Fluo-C2DL-Huh7\") / dataset\n",
    "image = imread(str(path / \"*.tif\"))\n",
    "\n",
    "if COLAB:\n",
    "    display(stackview.slice(image))\n",
    "else:\n",
    "    viewer.add_image(image)\n",
    "    screenshot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd11734d-2c9a-4aed-b593-d2a81152b399",
   "metadata": {},
   "source": [
    "## Cellpose Segmentation\n",
    "\n",
    "Use the Cellpose model to segment cells within each frame. The function predict applies Cellpose segmentation to each frame after normalizing it."
   ]
  },
  {
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   "source": [
    "cellpose = Cellpose(model_type=\"cyto2\", gpu=True)\n",
    "\n",
    "def predict(frame: ArrayLike, gamma: float) -> ArrayLike:\n",
    "    norm_frame = normalize(np.asarray(frame), gamma=gamma)\n",
    "    return cellpose(norm_frame, tile=False, normalize=False, diameter=75.0)\n",
    "\n",
    "cellpose_labels = np.zeros(image.shape, dtype=np.int32)\n",
    "array_apply(\n",
    "    image,\n",
    "    out_array=cellpose_labels,\n",
    "    func=predict,\n",
    "    gamma=0.5,\n",
    ")"
   ]
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   "source": [
    "## View Segmentations\n",
    "\n",
    "After obtaining segmented labels, visualize them alongside the original images. This helps to inspect the quality of the segmentation."
   ]
  },
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    "if COLAB:\n",
    "    display(stackview.curtain(image, cellpose_labels))\n",
    "else:\n",
    "    layer = viewer.add_labels(cellpose_labels)\n",
    "    screenshot()\n",
    "    layer.visible = False"
   ]
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   "source": [
    "## Extract Contours and Detection\n",
    "\n",
    "We converted the segmentation labels to contour and detection maps.\n",
    "These maps are the intermediate representation of used by Ultrack."
   ]
  },
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   "source": [
    "detection, contours = labels_to_edges(cellpose_labels, sigma=5.0)"
   ]
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    "if COLAB:\n",
    "    display(stackview.curtain(image, detection))\n",
    "else:\n",
    "    layer = viewer.add_labels(detection)\n",
    "    screenshot()\n",
    "    layer.visible = False"
   ]
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    "if COLAB:\n",
    "    display(stackview.curtain(image, contours))\n",
    "else:\n",
    "    layer = viewer.add_image(contours)\n",
    "    screenshot()\n",
    "    layer.visible = False"
   ]
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   "source": [
    "## Configuration\n",
    "\n",
    "Set tracking parameters with ultrack's `MainConfig`.\n",
    "\n",
    "The `track` procedure is composed of three steps that can also be called individually:\n",
    "- `segment`: Computes the segmentation hypotheses for tracking;\n",
    "- `link`: Links and assign edge weights to the segmentation hypotheses;\n",
    "- `solve`: Solves the tracking problem by selecting the strongly connected segmentation hypotheses.\n",
    "\n",
    "Each of these steps requires its own configuration, which we'll set up below. Its documentation can be found [here](https://github.com/royerlab/ultrack/blob/main/ultrack/config/README.md)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    },
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   "source": [
    "config = MainConfig()\n",
    "\n",
    "# Candidate segmentation parameters\n",
    "config.segmentation_config.n_workers = 4\n",
    "config.segmentation_config.min_area = 2500\n",
    "config.segmentation_config.min_frontier = 0.1\n",
    "\n",
    "# Setting the maximum number of candidate neighbors and maximum spatial distance between cells\n",
    "config.linking_config.max_neighbors = 5\n",
    "config.linking_config.max_distance = 100\n",
    "config.linking_config.n_workers = 4\n",
    "\n",
    "# Adding absurd weight to division because there are few dividing cells\n",
    "config.tracking_config.division_weight = -100\n",
    "\n",
    "# Very few tracks enter/leave the field of view, increasing penalization\n",
    "config.tracking_config.disappear_weight = -1\n",
    "config.tracking_config.appear_weight = -1\n",
    "\n",
    "print(config)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e87d2140-97e0-442c-99c5-0e53a191a2d8",
   "metadata": {},
   "source": [
    "## Tracking\n",
    "\n",
    "Run the tracking algorithm based on the provided configuration, detected regions, and contours."
   ]
  },
  {
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   "outputs": [],
   "source": [
    "track(\n",
    "    config,\n",
    "    detection=detection,\n",
    "    edges=contours,\n",
    "    overwrite=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## Exporting and Visualization\n",
    "\n",
    "The intermediate tracking data is stored on disk and must be exported to your preferred format.\n",
    "Here, we convert the resulting tracks to a DataFrame and Zarr to visualize using napari if running locally."
   ]
  },
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   "source": [
    "tracks_df, graph = to_tracks_layer(config)\n",
    "tracks_df.to_csv(f\"{dataset}_tracks.csv\", index=False)\n",
    "\n",
    "segments = tracks_to_zarr(\n",
    "    config,\n",
    "    tracks_df,\n",
    "    overwrite=True,\n",
    ")\n",
    "\n",
    "if COLAB:\n",
    "    display(stackview.curtain(image, segments))\n",
    "else:\n",
    "    viewer.add_tracks(\n",
    "        tracks_df[[\"track_id\", \"t\", \"y\", \"x\"]],\n",
    "        name=\"tracks\",\n",
    "        graph=graph,\n",
    "        visible=True,\n",
    "    )\n",
    "\n",
    "    viewer.add_labels(segments, name=\"segments\").contour = 2\n",
    "    screenshot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2106871-92c9-4581-8133-7726f5b645d7",
   "metadata": {},
   "source": [
    "## Run Metrics\n",
    "\n",
    "Finally, we evaluate the tracking performance using `traccuracy` with the metrics and annotations from the [Cell Tracking Challenge](celltrackingchallenge.net)."
   ]
  },
  {
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   "source": [
    "name = f\"{path.parent.name}_{path.name}\".upper()\n",
    "output_path = Path(name) / \"TRA\"\n",
    "to_ctc(output_path, config, overwrite=True)\n",
    "\n",
    "gt_path = path.parent / f\"{dataset}_GT\" / \"TRA\"\n",
    "\n",
    "run_metrics(\n",
    "    gt_data=load_ctc_data(gt_path),\n",
    "    pred_data=load_ctc_data(output_path),\n",
    "    matcher=CTCMatched,\n",
    "    metrics=[CTCMetrics],\n",
    ")[\"CTCMetrics\"]"
   ]
  }
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