diff --git "a/notebooks/aeropath-load-dataset-example.ipynb" "b/notebooks/aeropath-load-dataset-example.ipynb" new file mode 100644--- /dev/null +++ "b/notebooks/aeropath-load-dataset-example.ipynb" @@ -0,0 +1,1731 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "view-in-github" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "aB8AjX-DOLI1", + "outputId": "d25d5f60-1015-4f9a-9b4f-d6e06120f199" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m493.7/493.7 kB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m12.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + 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"\n", + "ct_image = nib.load(patient_1[\"ct\"])\n", + "ct_image = resample_to_output(ct_image, order=1)\n", + "ct_data = ct_image.get_fdata().astype(\"int32\")\n", + "\n", + "print(\"CT:\", ct_data.shape, ct_data.dtype)\n", + "\n", + "airways_image = nib.load(patient_1[\"airways\"])\n", + "airways_image = resample_to_output(airways_image, order=0)\n", + "airways_data = airways_image.get_fdata().astype(\"uint8\")\n", + "\n", + "print(\"Airways:\", airways_data.shape, airways_data.dtype)\n", + "\n", + "lungs_image = nib.load(patient_1[\"lungs\"])\n", + "lungs_image = resample_to_output(lungs_image, order=0)\n", + "lungs_data = lungs_image.get_fdata().astype(\"uint8\")\n", + "\n", + "print(\"Lungs:\", lungs_data.shape, lungs_data.dtype)\n", + "\n", + "# HU-clip CT for better contrast\n", + "ct_data[ct_data < -1024] = -1024\n", + "ct_data[ct_data > 400] = 400\n", + "\n", + "# plot corresponding CT, Airways, and Lungs for a single 2D slide side-by-side\n", + "fig, ax = plt.subplots(1, 3, figsize=(20, 12))\n", + "ax[0].imshow(ct_data[..., slice_nr], cmap=\"gray\")\n", + "ax[1].imshow(airways_data[..., slice_nr], cmap=\"gray\")\n", + "ax[2].imshow(lungs_data[..., slice_nr], cmap=\"gray\")\n", + "\n", + "for i, title in enumerate([\"CT\", \"Airways\", \"Lungs\"]):\n", + " ax[i].set_title(title)\n", + "plt.show()" + ] + } + ], + "metadata": { + "colab": { + "authorship_tag": "ABX9TyO+FqbZLfyG+4ejr+fhLd0A", + "include_colab_link": true, + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "0debf007a63b4bfca1abbf45132aa360": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": 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