Fix typo (#1813)

Fixes #1698
Fix typo and add 3d_image_transforms.ipnb test back (works well in
24.08)

### Checks
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [ ] Avoid including large-size files in the PR.
- [ ] Clean up long text outputs from code cells in the notebook.
- [ ] For security purposes, please check the contents and remove any
sensitive info such as user names and private key.
- [ ] Ensure (1) hyperlinks and markdown anchors are working (2) use
relative paths for tutorial repo files (3) put figure and graphs in the
`./figure` folder
- [ ] Notebook runs automatically `./runner.sh -t <path to .ipynb file>`

Signed-off-by: YunLiu <[email protected]>

README.md

@@ -316,22 +316,22 @@ This tutorial shows the use cases of training and validating a 3D Latent Diffusi
 ##### [2D latent diffusion model](./generative/2d_ldm)
 This tutorial shows the use cases of training and validating a 2D Latent Diffusion Model.
 
-#### [Brats 3D latent diffusion model](./3d_ldm/README.md)
+##### [Brats 3D latent diffusion model](./3d_ldm/README.md)
 Example shows the use cases of training and validating a 3D Latent Diffusion Model on Brats 2016&2017 data, expanding on the above notebook.
 
-#### [MAISI 3D latent diffusion model](./maisi/README.md)
+##### [MAISI 3D latent diffusion model](./maisi/README.md)
 Example shows the use cases of training and validating Nvidia MAISI (Medical AI for Synthetic Imaging) model, a 3D Latent Diffusion Model that can generate large CT images with paired segmentation masks, variable volume size and voxel size, as well as controllable organ/tumor size.
 
-#### [SPADE in VAE-GAN for Semantic Image Synthesis on 2D BraTS Data](./spade_gen)
+##### [SPADE in VAE-GAN for Semantic Image Synthesis on 2D BraTS Data](./spade_gen)
 Example shows the use cases of applying SPADE, a VAE-GAN-based neural network for semantic image synthesis, to a subset of BraTS that was registered to MNI space and resampled to 2mm isotropic space, with segmentations obtained using Geodesic Information Flows (GIF).
 
-#### [Applying Latent Diffusion Models to 2D BraTS Data for Semantic Image Synthesis](./spade_ldm)
+##### [Applying Latent Diffusion Models to 2D BraTS Data for Semantic Image Synthesis](./spade_ldm)
 Example shows the use cases of applying SPADE normalization to a latent diffusion model, following the methodology by Wang et al., for semantic image synthesis on a subset of BraTS registered to MNI space and resampled to 2mm isotropic space, with segmentations obtained using Geodesic Information Flows (GIF).
 
-#### [Diffusion Models for Implicit Image Segmentation Ensembles](./image_to_image_translation)
+##### [Diffusion Models for Implicit Image Segmentation Ensembles](./image_to_image_translation)
 Example shows the use cases of how to use MONAI for 2D segmentation of images using DDPMs. The same structure can also be used for conditional image generation, or image-to-image translation.
 
-#### [Evaluate Realism and Diversity of the generated images](./realism_diversity_metrics)
+##### [Evaluate Realism and Diversity of the generated images](./realism_diversity_metrics)
 Example shows the use cases of using MONAI to evaluate the performance of a generative model by computing metrics such as Frechet Inception Distance (FID) and Maximum Mean Discrepancy (MMD) for assessing realism, as well as MS-SSIM and SSIM for evaluating image diversity.
 
 #### [VISTA2D](./vista_2d)

generation/README.md

@@ -28,7 +28,7 @@ Example shows the use cases of training and validating a 3D Latent Diffusion Mod
 ## [MAISI 3D latent diffusion model](./maisi/README.md)
 Example shows the use cases of training and validating Nvidia MAISI (Medical AI for Synthetic Imaging) model, a 3D Latent Diffusion Model that can generate large CT images with paired segmentation masks, variable volume size and voxel size, as well as controllable organ/tumor size.
 
-## [SPADE in VAE-GAN for Semantic Image Synthesis on 2D BraTS Data](./spade_gen/spade_gen.ipynb)
+## [SPADE in VAE-GAN for Semantic Image Synthesis on 2D BraTS Data](./spade_gan/spade_gan.ipynb)
 Example shows the use cases of applying SPADE, a VAE-GAN-based neural network for semantic image synthesis, to a subset of BraTS that was registered to MNI space and resampled to 2mm isotropic space, with segmentations obtained using Geodesic Information Flows (GIF).
 
 ## [Applying Latent Diffusion Models to 2D BraTS Data for Semantic Image Synthesis](./spade_ldm/spade_ldm_brats.ipynb)

runner.sh

@@ -130,7 +130,6 @@ skip_run_papermill=("${skip_run_papermill[@]}" .*nuclick_training_notebook.ipynb
 skip_run_papermill=("${skip_run_papermill[@]}" .*nuclei_classification_infer.ipynb*)  # https://github.com/Project-MONAI/tutorials/issues/1542
 skip_run_papermill=("${skip_run_papermill[@]}" .*nuclick_infer.ipynb*)  # https://github.com/Project-MONAI/tutorials/issues/1542
 skip_run_papermill=("${skip_run_papermill[@]}" .*unet_segmentation_3d_ignite_clearml.ipynb*)  # https://github.com/Project-MONAI/tutorials/issues/1555
-skip_run_papermill=("${skip_run_papermill[@]}" .*3d_image_transforms.ipynb*)  # https://github.com/Project-MONAI/tutorials/issues/1698
 skip_run_papermill=("${skip_run_papermill[@]}" .*vista_2d_tutorial_monai.ipynb*)
 
 # output formatting

vista_2d/vista_2d_tutorial_monai.ipynb

@@ -65,7 +65,7 @@
     "!python -c \"import ipykernel\" || pip install -q ipykernel\n",
     "!python -c \"import cv2\" || pip install -q opencv-python-headless\n",
     "!python -c \"import tqdm\" || pip install -q tqdm\n",
-    "!python -c \"import numba\" || pip installß -q numba\n",
+    "!python -c \"import numba\" || pip install -q numba\n",
     "!python -c \"import segment_anything\" || pip install -q git+https://github.com/facebookresearch/segment-anything.git\n",
     "%matplotlib inline"
    ]