Update README (#5)

Author: eigenvivek

README.md

@@ -23,10 +23,9 @@ pip install diffdrrdata
 
 ## DiffDRR
 
-`DiffDRR` is an differentiable X-ray renderer used for solving inverse
-problems in tomographic imaging. If you find
-[`DiffDRR`](https://github.com/eigenvivek/DiffDRR/) useful in your work,
-please cite our paper:
+[`DiffDRR`](https://github.com/eigenvivek/DiffDRR/) is an differentiable
+X-ray renderer used for solving inverse problems in tomographic imaging.
+If you find `DiffDRR` useful in your work, please cite our paper:
 
     @inproceedings{gopalakrishnan2022fast,
       title={Fast auto-differentiable digitally reconstructed radiographs for solving inverse problems in intraoperative imaging},
@@ -42,16 +41,17 @@ please cite our paper:
 We provide APIs to load the following open-source datasets into
 `DiffDRR`:
 
-| **Dataset**                                                                | **Anatomy**      | **\# of Subjects** | **\# of 2D Images** | **CTs** | **X-rays** | Fiducials |
-|----------------------------------------------------------------------------|------------------|:------------------:|:-------------------:|:-------:|:----------:|:---------:|
-| [`DeepFluoro`](https://github.com/rg2/DeepFluoroLabeling-IPCAI2020)        | pelvis           |         6          |         366         |   ✅    |     ✅     |    ❌     |
-| [`Ljubljana`](https://lit.fe.uni-lj.si/en/research/resources/3D-2D-GS-CA/) | neurovasculature |         10         |         20          |   ✅    |     ✅     |    ✅     |
+| **Dataset**                                                                | **Anatomy**      | **\# of Subjects** | **\# of 2D Images** | **CTs** | **X-rays** | **GT Fiducials** |
+|----------------------------------------------------------------------------|------------------|:------------------:|:-------------------:|:-------:|:----------:|:----------------:|
+| [`DeepFluoro`](https://github.com/rg2/DeepFluoroLabeling-IPCAI2020)        | pelvis           |         6          |         366         |   ✅    |     ✅     |        ❌        |
+| [`Ljubljana`](https://lit.fe.uni-lj.si/en/research/resources/3D-2D-GS-CA/) | neurovasculature |         10         |         20          |   ✅    |     ✅     |        ✅        |
 
 If you use any of these datasets, please cite the original papers.
 
 ### `DeepFluoro`
 
-`DeepFluoro` ([**Grupp et al.,
+[`DeepFluoro`](https://github.com/rg2/DeepFluoroLabeling-IPCAI2020)
+([**Grupp et al.,
 2020**](https://link.springer.com/article/10.1007/s11548-020-02162-7))
 provides paired X-ray fluoroscopy images and CT volume of the pelvis.
 The data were collected from six cadaveric subjects at John Hopkins
@@ -105,7 +105,8 @@ plt.show()
 
 ### `Ljubljana`
 
-`Ljubljana` (**[Mitrovic et al.,
+[`Ljubljana`](https://lit.fe.uni-lj.si/en/research/resources/3D-2D-GS-CA/)
+(**[Mitrovic et al.,
 2013](https://ieeexplore.ieee.org/abstract/document/6507588)**) provides
 paired 2D/3D digital subtraction angiography (DSA) images. The data were
 collected from 10 patients undergoing endovascular image-guided

index_files/figure-commonmark/cell-2-output-1.png

@@ -37,8 +37,8 @@
    "source": [
     "## DiffDRR\n",
     "\n",
-    "`DiffDRR` is an differentiable X-ray renderer used for solving inverse problems in tomographic imaging.\n",
-    "If you find [`DiffDRR`](https://github.com/eigenvivek/DiffDRR/) useful in your work, please cite our paper:\n",
+    "[`DiffDRR`](https://github.com/eigenvivek/DiffDRR/) is an differentiable X-ray renderer used for solving inverse problems in tomographic imaging.\n",
+    "If you find `DiffDRR` useful in your work, please cite our paper:\n",
     "\n",
     "```\n",
     "@inproceedings{gopalakrishnan2022fast,\n",
@@ -60,7 +60,7 @@
     "\n",
     "We provide APIs to load the following open-source datasets into `DiffDRR`:\n",
     "\n",
-    "| **Dataset**                                                                | **Anatomy**        | **\\# of Subjects** | **\\# of 2D Images** | **CTs** | **X-rays** | Fiducials |\n",
+    "| **Dataset**                                                                | **Anatomy**        | **\\# of Subjects** | **\\# of 2D Images** | **CTs** | **X-rays** | **GT Fiducials** |\n",
     "|----------------------------------------------------------------------------|--------------------|:------------------:|:-------------------:|:-------:|:----------:|:---------:|\n",
     "| [`DeepFluoro`](https://github.com/rg2/DeepFluoroLabeling-IPCAI2020)        | pelvis             |         6          |         366         |   ✅    |     ✅     |    ❌     |\n",
     "| [`Ljubljana`](https://lit.fe.uni-lj.si/en/research/resources/3D-2D-GS-CA/) | neurovasculature   |         10         |         20          |   ✅    |     ✅     |    ✅     |\n",
@@ -75,13 +75,8 @@
    "source": [
     "### `DeepFluoro`\n",
     "\n",
-    "`DeepFluoro` ([**Grupp et al., 2020**](https://link.springer.com/article/10.1007/s11548-020-02162-7)) provides paired X-ray fluoroscopy images and CT volume of the pelvis. The data were collected from six cadaveric subjects at John Hopkins University. Ground truth camera poses were estimated with an offline registration process. A visualization of the X-ray / CT pairs in the `DeepFluoro` dataset is available [here](https://vivekg.dev/DiffDRR-Datasets/deepfluoro_camera_poses.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
+    "[`DeepFluoro`](https://github.com/rg2/DeepFluoroLabeling-IPCAI2020) ([**Grupp et al., 2020**](https://link.springer.com/article/10.1007/s11548-020-02162-7)) provides paired X-ray fluoroscopy images and CT volume of the pelvis. The data were collected from six cadaveric subjects at John Hopkins University. Ground truth camera poses were estimated with an offline registration process. A visualization of the X-ray / CT pairs in the `DeepFluoro` dataset is available [here](https://vivekg.dev/DiffDRR-Datasets/deepfluoro_camera_poses.html).\n",
+    "\n",
     "```\n",
     "@article{grupp2020automatic,\n",
     "  title={Automatic annotation of hip anatomy in fluoroscopy for robust and efficient 2D/3D registration},\n",
@@ -149,7 +144,7 @@
    "source": [
     "### `Ljubljana`\n",
     "\n",
-    "`Ljubljana` (**[Mitrovic et al., 2013](https://ieeexplore.ieee.org/abstract/document/6507588)**) provides paired 2D/3D digital subtraction angiography (DSA) images. The data were collected from 10 patients undergoing endovascular image-guided interventions at the University of Ljubljana. Ground truth camera poses were estimated by registering surface fiducial markers.\n",
+    "[`Ljubljana`](https://lit.fe.uni-lj.si/en/research/resources/3D-2D-GS-CA/) (**[Mitrovic et al., 2013](https://ieeexplore.ieee.org/abstract/document/6507588)**) provides paired 2D/3D digital subtraction angiography (DSA) images. The data were collected from 10 patients undergoing endovascular image-guided interventions at the University of Ljubljana. Ground truth camera poses were estimated by registering surface fiducial markers.\n",
     "\n",
     "```\n",
     "@article{pernus20133d,\n",