diff --git a/docs/guides/install.md b/docs/guides/install.md
index 0bb7829..cc8fb91 100644
--- a/docs/guides/install.md
+++ b/docs/guides/install.md
@@ -7,9 +7,15 @@ PyALFE can be configured to use either [Greedy](https://greedy.readthedocs.io/en
 Similarly, PyALFE can be configured to use [Convert3D](https://sourceforge.net/p/c3d/git/ci/master/tree/doc/c3d.md) or python native library [Nilearn](https://nilearn.github.io/stable/index.html) for image processing tasks.
 To use Greedy and Convert3d, these command line tools should be downloaded using the [download command](#download-models-and-tools).
 
+### Option 1: Pypi installation
+You can install pyalfe by running:
+```bash
+pip install pyalfe
+```
 
-You can either instal pyalfe in [development mode](#development-mode-installation) or [build and install](#build-and-install).
-### Option 1: Development mode installation
+You can either install pyalfe from source after cloning the repo using [development mode](#development-mode-installation) or [build and install](#build-and-install).
+
+### Option 2: Development mode installation
 
 First update the setuptools
 ```bash
@@ -19,6 +25,7 @@ pip install --upgrade setuptools
 Run the following command in the parent pyalfe directory:
 
 ```bash
+
 pip install -e .
 ```
 
@@ -32,7 +39,7 @@ pip install --upgrade build
 Run the following commands in the parent pyalfe directory to build the whl file and install pyalfe
 ```bash
 python -m build
-pip install dist/pyalfe-0.0.1-py3-none-any.whl
+pip install dist/pyalfe-*-py3-none-any.whl
 ```
 
 ### Download models
diff --git a/docs/guides/tutorial.md b/docs/guides/tutorial.md
index 49144d1..408f97a 100644
--- a/docs/guides/tutorial.md
+++ b/docs/guides/tutorial.md
@@ -22,7 +22,7 @@ mkdir -p pyalfe-output
 
 ## Installation
 
-Create a python virtual environment and activate
+Create a python virtual environment and activate it
 ```bash
 python3 -m venv venv
 source venv/bin/activate
@@ -41,5 +41,5 @@ pyalfe download models
 
 ## Run
 ```bash
-pyalfe run UPENNGBM0000511 --input-dir alfe/input  --output-dir alfe/output
+pyalfe run UPENNGBM0000511 --input-dir pyalfe-test-data  --output-dir pyalfe-output
 ```
diff --git a/docs/guides/usage.md b/docs/guides/usage.md
index 149242b..4a0c638 100644
--- a/docs/guides/usage.md
+++ b/docs/guides/usage.md
@@ -1,5 +1,8 @@
 ## Usage
 
+We recommend running the configure command first to create a configuration file for the pipeline.
+However, you can skip to [Running the pipeline](#running-the-pipeline) section if you prefer to provide all the required configuration through command line
+options.
 ### Configuration
 To configrue the PyALFE pipeline you should run:
 ```bash
@@ -7,32 +10,12 @@ pyalfe configure
 ```
 which prompt the you to enter the following required configurations:
 
-#### Classified directory
+#### Input directory
 ```bash
 Enter input image directory: /path/to/my_mri_data
 ```
-The classified directory (`classified_dir`) is the input directory to PyALFE and should be organized by accessions (or session ids). Inside the directory for each accession there should be a directory for each available modality.
-Here is an example that follow ALFE default structure:
-
-```
-my_mri_data
-│
-│───anat
-│   │───sub-123_T1w.nii.gz
-│   │───sub-123_T2w.nii.gz
-│   └───sub-123_FLAIR.nii.gz
-│───dwi
-│    │───sub-123_dwi.nii.gz
-│    └───sub-123_md.nii.gz
-│───swi
-│    └───sub-123_swi.nii.gz
-└───perf
-     └───sub-123_cbf.nii.gz
-
-```
-To use this directory the user should provide `path/to/my_mri_data` as the classified directory. This config value can be overwritten when calling `pyalfe run` via `-cd` or `--classified-dir` option.
-
-pyALFE also supports BIDS directories. Here is an example of input dir organized in BIDS format:
+The input directory (`input_dir`) contains the images that will be processed by PyALFE and should be organized by accessions (or session ids). Inside the directory for each accession there should be a directory for each available modality.
+Here is an example that follows ALFE default structure:
 ```
 my_mri_data
 │
@@ -46,9 +29,10 @@ my_mri_data
 │   │   └── FLAIR.nii.gz
 │   │───ADC
 │   │   └── ADC.nii.gz
-│   └───T2
-│       └── T2.nii.gz
-│
+│   │───T2
+│   │   └── T2.nii.gz
+│   └───CBF
+│       └── CBF.nii.gz
 └───12356
 .   │
 .   │───T1
@@ -62,13 +46,51 @@ my_mri_data
     └───T2
         └── T2.nii.gz
 ```
-#### Processed directory
+To use this directory the user should provide `path/to/my_mri_data` as the input directory. This config value can be overwritten when calling `pyalfe run` via `-id` or `--input-dir` option.
+
+pyALFE also supports BIDS directories. Here is an example of input dir organized in BIDS format:
+
+```
+my_mri_data
+│
+│───sub-01
+│   │───anat
+│   │   │───sub-01_T1w.nii.gz
+│   │   │───sub-01_ce-gadolinium_T1w.nii.gz
+│   │   │───sub-01_T2w.nii.gz
+│   │   └───sub-01_FLAIR.nii.gz
+│   │───dwi
+│   │    │───sub-01_dwi.nii.gz
+│   │    └───sub-01_md.nii.gz
+│   │───swi
+│   │    └───sub-01_swi.nii.gz
+│   └───perf
+│       └───sub-01_cbf.nii.gz
+│
+└───sub-02
+.   │───anat
+.   │   │───sub-02_T1w.nii.gz
+.   │   │───sub-02_ce-gadolinium_T1w.nii.gz
+    │   │───sub-02_T2w.nii.gz
+    │   └───sub-02_FLAIR.nii.gz
+    │───dwi
+    │    │───sub-02_dwi.nii.gz
+    │    └───sub-02_md.nii.gz
+    │───swi
+    │    └───sub-02_swi.nii.gz
+    └───perf
+        └───sub-02_cbf.nii.gz
+
+```
+
+#### Output directory
 ```bash
-Enter input image directory: /path/to/processed_data_dir
+Enter output image directory: /path/to/output_dir
 ```
-The processed image directory (`processed_dir`) is where ALFE writes all its output to.
+The output image directory (`output_dir`) is where pyALFE writes all its output to.
 It can be any valid path in filesystem that user have write access to.
-This config value can be overwritten when calling `pyalfe run` via `-pd` or `--processed-dir` option.
+This config value can be overwritten when calling `pyalfe run` via `-od` or `--output-dir` option.
+
 
 #### Modalities
 ```bash
@@ -102,7 +124,7 @@ image processor to use (c3d, nilearn) [c3d]:
 ```
 Currently, pyalfe can be configures to use either Convert3D (a.k.a. c3d) or Nilearn for image processing tasks.
 The default is Convert3d aka c3d. In other to use c3d,
-you have to download it using the [download command](#download-models-and-tools).
+you have to download it using the [download command](#download-models).
 To use Nilearn, you do not need to run any extra command since it is already installed when you install pyalfe.
 This config value can be overwritten when calling `pyalfe run` via `-ip` or `--image_processing` option.
 
@@ -111,10 +133,18 @@ This config value can be overwritten when calling `pyalfe run` via `-ip` or `--i
 image registration to use (greedy, ants) [greedy]:
 ```
 Currently, pyalfe can be configures to use either greedy or ants for image registration tasks. The default is greedy.
-In other to use greedy, you have to download it using the [download command](#download-models-and-tools). To use ants,
+In other to use greedy, you have to download it using the [download command](#download-models). To use ants,
 install pyalfe with ants support ``pip install pyalfe[ants]``.
 This config value can be overwritten when calling `pyalfe run` via `-ir` or `--image-registration` option.
 
+#### Dierctory Data Structure
+```bash
+data directory structure (press enter for default) (alfe, bids) [alfe]:
+```
+
+The directory structure that pyALFE expects in the input directory and will follow when creating the output. See [Inupt directory](#input-directory) for information on ALFE and BIDS.
+This config value can be overwritten when calling `payalfe run` via `-dds` or `--data-dir-structure` option.
+
 ### Running the pipeline
 To run PyALFE for an accession
 
diff --git a/docs/intro.md b/docs/intro.md
index 9aa3d27..d1fa85b 100644
--- a/docs/intro.md
+++ b/docs/intro.md
@@ -1,7 +1,7 @@
 # PyALFE
 
-PyALFE is an open-source python Automated Lesion and Feature Extraction pipeline.
-It consumes a clinical brain MRI study consisting of various pulse sequences
+PyALFE is an open-source python pipeline for Automated Lesion and Feature Extraction.
+It can process a clinical brain MRI study consisting of various sequences
 such as T1, T1 post contrast, FLAIR, T2, ADC, and CBF. It performs
 pre-processing, inter-seq registration, template registration, segmentation,
 and quantification to generate a set of numerical human-interpretable features