Merge pull request #53 from reghbali/reghbali_remove_dep_inj

Reghbali remove dep inj

README.md

@@ -239,6 +239,16 @@ 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.
 
+#### Tissue Segmentation
+```bash
+tissue segmentation method (press enter for default) (prior, synthseg) [prior]:
+```
+
+The tissue segmentation method that will be used by pyALFE. The default is based on the method described in this [paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8346689/), a UNet
+that receives a template-based **prior** map tissue alongside the image . pyALFE
+also supports [SynthSeg](https://www.sciencedirect.com/science/article/pii/S1361841523000506) via [FreeSurfer](https://surfer.nmr.mgh.harvard.edu/fswiki/SynthSeg). To use SynthSeg, you have to have FreeSurfer >= v7
+installed.
+
 ### Running the pipeline
 To run PyALFE for an accession
 

docs/api/main.rst

@@ -1,6 +1,7 @@
 pyalfe.main
 ===========
 
-.. click:: pyalfe.main:main
-  :prog: pyalfe
-  :nested: full
+.. automodule:: pyalfe.main
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/guides/tutorial.md

@@ -1,8 +1,7 @@
 # Tutorial
 
-In this tutorial, you will download sample data, install pyalfe, and run it on this data.
-This tutorial requires `git`, `python>=3.9`, [greedy](https://sourceforge.net/projects/greedy-reg/files/),
-and [c3d](https://sourceforge.net/projects/c3d/).
+In this tutorial, you will download sample data, install pyalfe, configure it, and run it on this data.
+This tutorial requires `git`, `python>=3.9`, [greedy](https://sourceforge.net/projects/greedy-reg/files/).
 
 
 ## Data download and directory setup
@@ -15,7 +14,7 @@ This will create a directory named `pyalfe-test-data` and downloads the tutorial
 data inside it. The data is the MRI scan of a glioblastoma patient and is
 taken from the [UPenn-GBM dataset](https://www.nature.com/articles/s41597-022-01560-7).
 
-2. Create an output directory where you have write access
+2. Create the output directory
 ```bash
 mkdir -p pyalfe-output
 ```
@@ -38,8 +37,46 @@ Download models
 pyalfe download models
 ```
 
+Configure pyalfe by running
+```bash
+pyalfe configrue
+```
+and simply pressing enter for all the prompts for default values.
 
 ## Run
 ```bash
 pyalfe run UPENNGBM0000511 --input-dir pyalfe-test-data  --output-dir pyalfe-output
 ```
+
+## Pipeline output
+You can now inspect the pipeline output by changing directory to
+
+```bash
+cd pyalfe-output/UPENNGBM0000511
+```
+and exploring the `FLAIR`, `T1`, `T1Post`, `T2`, `ADC` subdirectories.
+
+For instance, the individual FLAIR lesion measures can be found at:
+```bash
+FLAIR/quantification/UPENNGBM0000511_SummaryLesionMeasures.csv
+```
+
+FLAIR lesion segmentation can be found at:
+```bash
+FLAIR/abnormalmap/UPENNGBM0000511_FLAIR_abnormal_seg.nii.gz
+```
+
+Summary T1Post (enhancing) lesion measures can be found at:
+```bash
+T1Post/quantification/UPENNGBM0000511_IndividualLesionMeasures.csv
+```
+
+Brain volumetric measures can be found at:
+```bash
+T1/quantification/UPENNGBM0000511_volumeMeasures.csv
+```
+
+The tissue segmentation can be found at:
+```bash
+T1/UPENNGBM0000511_T1_tissue_seg.nii.gz
+```

docs/guides/usage.md

@@ -4,7 +4,7 @@ We recommend running the configure command first to create a configuration file
 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:
+To configure the PyALFE pipeline, you should run:
 ```bash
 pyalfe configure
 ```
@@ -120,31 +120,42 @@ This config value can be overwritten when calling `pyalfe run` via `-dt` or `--d
 
 #### Image processor
 ```bash
-image processor to use (c3d, nilearn) [c3d]:
+image processor to use (nilearn, c3d) [nilearn]:
 ```
-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).
-To use Nilearn, you do not need to run any extra command since it is already installed when you install pyalfe.
+Currently, pyalfe can be configured to use either Nilearn, Convert3D (a.k.a. c3d) for image processing tasks.
+The default is Nilearn which is a python native library and is installed during installation of pyalfe.
+To use c3d, you have to download and install it (https://sourceforge.net/projects/c3d/) on your machine.
+
 This config value can be overwritten when calling `pyalfe run` via `-ip` or `--image_processing` option.
 
 #### Image Registration
 ```bash
 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). To use ants,
+Currently, pyalfe can be configured to use either greedy or ants for image registration tasks. The default is greedy.
+In other to use greedy, you have to download and install greedy (https://sourceforge.net/projects/greedy-reg/). 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
+#### Directory 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.
 
+#### Tissue Segmentation
+```bash
+tissue segmentation method (press enter for default) (prior, synthseg) [prior]:
+```
+
+The tissue segmentation method that will be used by pyALFE. The default is based on the method described in this [paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8346689/), a UNet
+that receives a template-based **prior** map tissue alongside the image . pyALFE
+also supports [SynthSeg](https://www.sciencedirect.com/science/article/pii/S1361841523000506) via [FreeSurfer](https://surfer.nmr.mgh.harvard.edu/fswiki/SynthSeg). To use SynthSeg, you have to have FreeSurfer >= v7
+installed.
+
 ### Running the pipeline
 To run PyALFE for an accession
 

pyalfe/config.ini

@@ -7,3 +7,4 @@ dominant_tissue=white_matter
 image_processor=c3d
 image_registration=greedy
 data_dir_structure = alfe
+tissue_segmentation = prior