Merge pull request #193 from pyplati/dose-visualisation

Add dose visualisation function
pyplati · Feb 28, 2023 · 46e8f80 · 46e8f80
2 parents 4ecceff + e50dfc8
commit 46e8f80
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diff --git a/examples/dvh_analysis.ipynb b/examples/dvh_analysis.ipynb
@@ -20,7 +20,7 @@
     "try:\n",
     "    import platipy\n",
     "except:\n",
-    "    !pip install git+https://github.com/pyplati/platipy.git\n",
+    "    !pip install platipy\n",
     "    import platipy\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
@@ -31,7 +31,8 @@
     "from platipy.imaging.tests.data import get_hn_nifti\n",
     "from platipy.imaging import ImageVisualiser\n",
     "from platipy.imaging.label.utils import get_com\n",
-    "from platipy.imaging.dose.dvh import calculate_dvh_for_labels, calculate_d_x, calculate_v_x"
+    "from platipy.imaging.dose.dvh import calculate_dvh_for_labels, calculate_d_x, calculate_v_x\n",
+    "from platipy.imaging.visualisation.dose import visualise_dose"
    ]
   },
   {
@@ -102,7 +103,7 @@
    "source": [
     "vis = ImageVisualiser(ct_image, cut=get_com(structures[\"PTV60\"]))\n",
     "\n",
-    "vis.add_scalar_overlay(dose, discrete_levels=20, colormap=plt.cm.get_cmap(\"inferno\"))\n",
+    "vis.add_scalar_overlay(dose, discrete_levels=20, colormap=plt.cm.get_cmap(\"inferno\"), name=\"Dose (Gy)\")\n",
     "vis.add_contour(structures)\n",
     "\n",
     "fig = vis.show()"
@@ -128,12 +129,22 @@
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dvh"
+   ]
+  },
+  {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Plot DVH\n",
     "\n",
-    "using the pandas DataFrame, we plot the DVH here"
+    "using the pandas DataFrame, we plot the DVH here. The DVH first needs to be reshaped to prepare it for plotting."
    ]
   },
   {
@@ -142,12 +153,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt_dvh = dvh.melt(id_vars=[\"label\", \"cc\", \"mean\"], var_name=\"bin\", value_name=\"dose\")\n",
-    "fig, ax = plt.subplots()\n",
+    "# Reshape the DVH\n",
+    "plt_dvh = dvh\n",
+    "plt_dvh = plt_dvh.set_index(\"label\")\n",
+    "plt_dvh = plt_dvh.iloc[:,3:].transpose()\n",
     "\n",
-    "for key, grp in plt_dvh.groupby(['label']):\n",
-    "    ax = grp.plot(ax=ax, kind='line', x='bin', y='dose', label=key)\n",
+    "# Plot the DVH\n",
+    "fig, ax = plt.subplots()\n",
+    "plt_dvh.plot(ax=ax, kind=\"line\", colormap=plt.cm.get_cmap(\"rainbow\"), legend=False)\n",
     "\n",
+    "# Add labels and show plot\n",
     "plt.legend(loc='best')\n",
     "plt.xlabel(\"Dose (Gy)\")\n",
     "plt.ylabel(\"Frequency\")\n",
@@ -156,12 +171,13 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## DVH Metrics\n",
     "\n",
-    "Finally, we extract commonly used metrics from the DVH, such as D95 or V40."
+    "Finally, we extract commonly used metrics from the DVH. In the following cells we extract the D0 and D95 as well as the V5 and V20."
    ]
   },
   {
@@ -170,7 +186,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_metrics_d = calculate_d_x(dvh, 95)\n",
+    "df_metrics_d = calculate_d_x(dvh, [0, 95])\n",
     "df_metrics_d"
    ]
   },
@@ -180,14 +196,52 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_metrics_v = calculate_v_x(dvh, 40)\n",
+    "df_metrics_v = calculate_v_x(dvh, [5, 20])\n",
     "df_metrics_v"
    ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dose and DVH visualisation\n",
+    "\n",
+    "The `visualise_dose` function can produce a visualisation including the DVH and dose metrics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, df_metrics = visualise_dose(\n",
+    "    ct_image,\n",
+    "    dose,\n",
+    "    structures,\n",
+    "    dvh=dvh,\n",
+    "    d_points=[0, 95],\n",
+    "    v_points=[5],\n",
+    "    d_cc_points=[10],\n",
+    "    structure_for_limits=dose>5,\n",
+    "    expansion_for_limits=40,\n",
+    "    contour_cmap=plt.cm.get_cmap(\"rainbow\"),\n",
+    "    dose_cmap=plt.cm.get_cmap(\"inferno\"),\n",
+    "    title=\"TCGA_CV_5977 Dose Metrics\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2.7.16 64-bit",
+   "display_name": "platipy-Ut1mjAmS-py3.8",
    "language": "python",
    "name": "python3"
   },
@@ -201,11 +255,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "2.7.16"
+   "version": "3.8.0"
   },
   "vscode": {
    "interpreter": {
-    "hash": "767d51c1340bd893661ea55ea3124f6de3c7a262a8b4abca0554b478b1e2ff90"
+    "hash": "950d949bcc8447743ba2e2f4f81e1f06919534c5bd1d053b1a2c5f1f73baee7b"
    }
   }
  },