Revise index.rst

Signed-off-by: Gigon Bae <gbae@nvidia.com>

docs/Makefile

@@ -4,7 +4,7 @@
 # You can set these variables from the command line.
 SPHINXOPTS    =
 SPHINXBUILD   = sphinx-build
-SPHINXPROJ    = cuImage
+SPHINXPROJ    = cuCIM
 SOURCEDIR     = source
 BUILDDIR      = build
 

docs/source/index.rst

@@ -5,57 +5,77 @@ cuCIM (Compute Unified Device Architecture Clara IMage) is an open-source, accel
 
 cuCIM provides GPU-accelearted I/O,
 computer vision and image processing primitives for N-Dimensional images including:
-*color conversion, 
-*exposure, 
-*feature extraction, 
-*filters, 
-*measure, 
-*metrics, 
-*morphology, 
-*registration, 
-*restoration, 
-*segmentation, and 
-*transforms.   
+
+- color conversion
+- exposure
+- feature extraction
+- filters
+- measure
+- metrics
+- morphology
+- registration
+- restoration
+- segmentation
+- transforms
 
 cuCIM supports the following formats:
-*Aperio ScanScope Virtual Slide (SVS), 
-*Deflated Compressed, 
-*Red Green Blue (RGB), 
-*Generic TIFF, 
-*JPEG/JPEG 2000 Compressed RGB,
-*Lempel-Ziv-Welch (LZW) RGB,
-*Philips TIFF, and
-*Tiled, Multi–Resolution TIFF. 
+
+-  Aperio ScanScope Virtual Slide (SVS)
+-  Philips TIFF
+-  Generic Tiled, Multi-resolution RGB TIFF files with the following
+   compression schemes:
+
+   -  No Compression
+   -  JPEG
+   -  JPEG2000
+   -  Lempel-Ziv-Welch (LZW)
+   -  Deflate
 
 Our API mirrors `scikit-image
 <https://scikit-image.org/>`_ for image manipulation and `OpenSlide
 <https://openslide.org/>`_ for image loading.
 
-cuCIM is interoperable with the following workflows: 
-*Albumentations, 
-*cuPY, 
-*Data Loading Library (DALI), 
-*JFX, 
-*MONAI, 
-*Numba, 
-*NumPy, 
-*PyTorch, 
-*Tensorflow, and
-*Triton.
-
-cuCIM is fully open sourced under the Apache-2.0 license, and the Clara and RAPIDS teams welcomes new and seasoned
-contributors, users and hobbyists! You may download cuCIM via Anaconda [Conda](https://anaconda.org/rapidsai-nightly/cucim) or [PyPi](https://pypi.org/project/cucim/) Thank you for your wonderful support! Below, we provide some resources to help get you started. 
+cuCIM is interoperable with the following workflows:
+
+- Albumentations
+- cuPY
+- Data Loading Library (DALI)
+- JFX
+- MONAI
+- Numba
+- NumPy
+- PyTorch
+- Tensorflow
+- Triton
+
+cuCIM is fully open sourced under the Apache-2.0 license, and the Clara
+and RAPIDS teams welcomes new and seasoned contributors, users and
+hobbyists! You may download cuCIM via Anaconda `Conda`_ or `PyPI`_ Thank
+you for your wonderful support! Below, we provide some resources to help
+get you started.
 
 **Blogs**
-[Accelerating Scikit-Image API with cuCIM: n-Dimensional Image Processing and IO on GPUs](https://developer.nvidia.com/blog/cucim-rapid-n-dimensional-image-processing-and-i-o-on-gpus/)
-[Accelerating Digital Pathology Pipelines with NVIDIA Clara™ Deploy](https://developer.nvidia.com/blog/accelerating-digital-pathology-pipelines-with-nvidia-clara-deploy-2/)
+
+-  `Accelerating Scikit-Image API with cuCIM: n-Dimensional Image
+   Processing and IO on GPUs`_
+-  `Accelerating Digital Pathology Pipelines with NVIDIA Clara™ Deploy`_
 
 **Webinars**
-[cuCIM: a GPU Image IO and Processing Library](https://www.youtube.com/watch?v=G46kOOM9xbQ)
+
+-  `cuCIM: a GPU Image IO and Processing Library`_
+
+.. _Conda: https://anaconda.org/rapidsai-nightly/cucim
+.. _PyPi: https://pypi.org/project/cucim/
+.. _`Accelerating Scikit-Image API with cuCIM: n-Dimensional Image Processing and IO on GPUs`: https://developer.nvidia.com/blog/cucim-rapid-n-dimensional-image-processing-and-i-o-on-gpus/
+.. _Accelerating Digital Pathology Pipelines with NVIDIA Clara™ Deploy: https://developer.nvidia.com/blog/accelerating-digital-pathology-pipelines-with-nvidia-clara-deploy-2/
+.. _`cuCIM: a GPU Image IO and Processing Library`: https://www.youtube.com/watch?v=G46kOOM9xbQ
+
+
+Contents
+==================
 
 .. toctree::
    :maxdepth: 4
-   :caption: Contents:
 
    api.rst