Merge pull request #4 from BIDS-Apps/develop

Merge develop changes

Dockerfile

@@ -1,32 +1,37 @@
 # Use an image with pre-built ANTs included
-FROM gdevenyi/magetbrain-bids-ants:21d7c12ee1e332827b04848eb5f70f55d14cac23
+FROM gdevenyi/magetbrain-bids-ants:82dcdd647211004f3220e4073ea4daf06fdf89f9
 
 RUN apt-get update \
-    && apt-get install --auto-remove --no-install-recommends -y parallel \
+    && apt-get install --auto-remove --no-install-recommends -y parallel git curl gzip bzip2 gnupg2 unzip coreutils ca-certificates \
     && rm -rf /var/lib/apt/lists/*
 
-RUN apt-get update \
-    && apt-get install -y --no-install-recommends --auto-remove git curl unzip bzip2 \
-    && curl -o anaconda.sh https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh \
-    && bash anaconda.sh -b -p /opt/anaconda && rm -f anaconda.sh \
-    && git clone  https://github.com/CobraLab/antsRegistration-MAGeT.git /opt/antsRegistration-MAGeT \
-    && (cd /opt/antsRegistration-MAGeT && git checkout tags/v0.2.2.1) \
-    && curl -o /opt/atlases-nifti.zip -sL http://cobralab.net/files/atlases-nifti.zip \
+RUN curl --insecure -o anaconda.sh https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh \
+    && bash anaconda.sh -b -p /opt/anaconda && rm -f anaconda.sh
+
+RUN curl -o /opt/atlases-nifti.zip -sL http://cobralab.net/files/atlases-nifti.zip \
     && mkdir /opt/atlases-nifti \
     && unzip /opt/atlases-nifti.zip -d /opt \
     && curl -sL http://cobralab.net/files/brains_t1_nifti.tar.bz2 | tar xvj -C /opt/atlases-nifti \
     && curl -o /opt/atlases-nifti/colin.zip -sL http://packages.bic.mni.mcgill.ca/mni-models/colin27/mni_colin27_1998_nifti.zip \
     && mkdir /opt/atlases-nifti/colin && unzip /opt/atlases-nifti/colin.zip -d /opt/atlases-nifti/colin && rm -f /opt/atlases-nifti/colin.zip \
-    && curl -sL https://deb.nodesource.com/setup_4.x | bash - \
-    && apt-get install -y nodejs \
-    && apt-get purge --auto-remove -y curl unzip bzip2 \
+    && gzip /opt/atlases-nifti/colin/colin27_t1_tal_lin.nii
+
+RUN curl --insecure -sL https://deb.nodesource.com/setup_10.x | bash - \
+    && apt-get install -y --no-install-recommends --auto-remove nodejs \
     && rm -rf /var/lib/apt/lists/*
 
 ENV CONDA_PATH "/opt/anaconda"
 
-RUN /opt/anaconda/bin/pip install git+https://github.com/pipitone/qbatch.git@aade5b9a17c5a5a2fe6b28267b3bca10b05a5936
+RUN /opt/anaconda/bin/conda config --append channels conda-forge
+RUN /opt/anaconda/bin/conda install -y numpy scipy nibabel pandas
+RUN /opt/anaconda/bin/pip install future six
+RUN /opt/anaconda/bin/pip install duecredit
+RUN /opt/anaconda/bin/pip install pybids
+RUN npm install -g [email protected] --unsafe-perm
 
-RUN npm install -g [email protected]
+RUN git clone https://github.com/CobraLab/antsRegistration-MAGeT.git /opt/antsRegistration-MAGeT && \
+    (cd /opt/antsRegistration-MAGeT && git checkout tags/v0.3.1)
+RUN /opt/anaconda/bin/pip install git+https://github.com/pipitone/qbatch.git@951dd1bdfdcbb5fd3f27ee6a3e261eaecac1ef70
 
 ENV PATH /opt/ANTs/bin:/opt/anaconda/bin:/opt/antsRegistration-MAGeT/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
 ENV QBATCH_SYSTEM local

README.md

@@ -1,14 +1,14 @@
 ## MAGeTbrain segmentation pipeline
 
 ### Description
-This pipeline takes in native-space T1 or T2 (or multiple co-registered modalities) brain images and volumetrically segments
-them using the MAGeTbrain algorithm.
+This pipeline takes in native-space T1 brain images and volumetrically segments
+them using the MAGeTbrain algorithm using a variety of input atlases.
 
 ### Documentation
-Provide a link to the documention of your pipeline.
+https://github.com/cobralab/antsRegistration-MAGet.
 
 ### How to report errors
-Provide instructions for users on how to get help and report errors.
+Please open an issue at https://github.com/BIDS-Apps/MAGeTbrain/issues
 
 ### Acknowledgements
 Describe how would you would like users to acknowledge use of your App in their papers (citation, a paragraph that can be copy pasted, etc.)
@@ -21,24 +21,24 @@ usage: run.py [-h]
               [--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]
               [--segmentation_type {amygdala,cerebellum,hippocampus-whitematter,colin27-subcortical,all}]
               [-v] [--n_cpus N_CPUS] [--fast] [--label-masking] [--no-cleanup]
-              bids_dir output_dir {participant1,participant2,group}
+              bids_dir output_dir {participant1,participant2}
 
 MAGeTbrain BIDS App entrypoint script.
 
 positional arguments:
   bids_dir              The directory with the input dataset formatted
                         according to the BIDS standard.
   output_dir            The directory where the output files should be stored.
-                        When you are running group level analysis this folder
+                        When you are running partipant2 level analysis this folder
                         must be prepopulated with the results of
-                        theparticipant level analysis.
-  {participant1,participant2,group}
+                        the participant1 level analysis.
+  {participant1,participant2}
                         Level of the analysis that will be performed. Multiple
-                        participant level analyses can be run independently
-                        (in parallel) using the same output_dir. In MAGeTbrain
-                        parlance, participant1 = template stage, partipant2 =
-                        subject stage group = resample + vote + qc stage. The
-                        proper order is participant1, participant2, group
+                        participant{1,2} level analyses can be run
+                        independently (in parallel) using the same output_dir.
+                        In MAGeTbrain parlance, participant1 = template stage,
+                        partipant2 = subject + resample + vote + qc stage. The
+                        proper order is participant1, participant2
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -53,35 +53,39 @@ optional arguments:
                         The segmentation label type to be used.
                         colin27-subcortical, since it is on a different atlas,
                         is not included in the all setting and must be run
-                        seperately
+                        separately
   -v, --version         show program's version number and exit
   --n_cpus N_CPUS       Number of CPUs/cores available to use.
   --fast                Use faster (less accurate) registration calls
   --label-masking       Use the input labels as registration masks to reduce
-                        computation and (possibily) improve registration
+                        computation and (possibly) improve registration
   --no-cleanup          Do no cleanup intermediate files after group phase
 ```
 
-To run it in participant level mode (for one participant):
+To run construct the template library, run the participant1 stage:
 ```sh
     docker run -i --rm \
 		-v /Users/filo/data/ds005:/bids_dataset:ro \
 		-v /Users/filo/outputs:/outputs \
 		bids/example \
-		/bids_dataset /outputs participant --participant_label 01
+		/bids_dataset /outputs participant1 --participant_label 01
 ```
-After doing this for all subjects (potentially in parallel), the group level analysis
+
+After doing this for approximately 21 representative subjects (potentially in parallel),
+the subject level labeling can be done:
 can be run:
 ```sh
     docker run -i --rm \
 		-v /Users/filo/data/ds005:/bids_dataset:ro \
 		-v /Users/filo/outputs:/outputs \
-		bids/example \
-		/bids_dataset /outputs group
+		bids/example /outputs participants2 --participant_label 01
 ```
-### Special considerations
-Describe whether your app has any special requirements. For example:
+This can also happen in parallel on a per-subject basis
 
-- Multiple map reduce steps (participant, group, participant2, group2 etc.)
-- Unusual memory requirements
-- etc.
+### Special considerations
+- segmentation_types output directories must be kept separate for each type
+- participant1 stages can be run in parallel per subject, approximately 21
+subjects should be selected which are a representative subset of the population
+under study
+- participant2 stages can also be run in parallel, but must be started after
+participant1 stages are complete

ants-build/Dockerfile

@@ -1,17 +1,15 @@
 # Use phusion/baseimage as base image
-FROM phusion/baseimage:0.10.1
-
-# Use baseimage-docker's init system.
-CMD ["/sbin/my_init"]
+FROM ubuntu:latest
 
 ENV DEBIAN_FRONTEND noninteractive
 
 RUN buildDeps='cmake build-essential git zlib1g-dev' \
     && apt-get update \
     && apt-get install -y $buildDeps --no-install-recommends \
+    && apt-get install -y ca-certificates \
     && rm -rf /var/lib/apt/lists/* \
     && git clone https://github.com/stnava/ANTs.git /opt/ANTs-src \
-    && cd /opt/ANTs-src && git checkout 21d7c12ee1e332827b04848eb5f70f55d14cac23 \
+    && cd /opt/ANTs-src && git checkout 82dcdd647211004f3220e4073ea4daf06fdf89f9 \
     && mkdir /opt/ANTs-src/build && cd /opt/ANTs-src/build \
     && cmake -DCMAKE_LINKER=/usr/bin/gold -DITK_BUILD_MINC_SUPPORT:BOOL=ON \
     -DBUILD_TESTING:BOOL=OFF -DRUN_LONG_TESTS:BOOL=OFF -DRUN_SHORT_TESTS:BOOL=OFF \

circle.yml

@@ -1,6 +1,7 @@
 general:
   artifacts:
-    - "~/outputs"
+    - "~/outputs-colin"
+    - "~/outputs-colin-labelmask"
 
 machine:
   services:
@@ -20,23 +21,32 @@ dependencies:
         timeout: 21600
     - mkdir -p ~/docker; docker save "bids/${CIRCLE_PROJECT_REPONAME,,}" > ~/docker/image.tar :
         timeout: 21600
-    - mkdir -p ${HOME}/outputs
+    - mkdir -p ${HOME}/outputs-colin
+    - mkdir -p ${HOME}/outputs-colin-labelmask
 
 test:
   override:
     # print version
     - docker run -ti --rm --read-only -v /tmp:/tmp -v /var/tmp:/var/tmp -v ${HOME}/data/ds003_downsampled:/bids_dataset bids/${CIRCLE_PROJECT_REPONAME,,} --version
-    # template level run for downsampled dataset
-    - docker run -ti --rm --read-only -v /tmp:/tmp -v /var/tmp:/var/tmp -v ${HOME}/data/ds003_downsampled:/bids_dataset -v ${HOME}/outputs:/outputs bids/${CIRCLE_PROJECT_REPONAME,,} --fast --n_cpus 2 --segmentation_type colin27-subcortical /bids_dataset /outputs participant1 --participant_label 01 :
+    # template level run for downsampled dataset no masking
+    - docker run -ti --rm --read-only -v /tmp:/tmp -v /var/tmp:/var/tmp -v ${HOME}/data/ds003_downsampled:/bids_dataset -v ${HOME}/outputs-colin:/outputs bids/${CIRCLE_PROJECT_REPONAME,,} --n_cpus 2 --segmentation_type colin27-subcortical /bids_dataset /outputs participant1 --participant_label 01 :
         timeout: 21600
-    - docker run -ti --rm --read-only -v /tmp:/tmp -v /var/tmp:/var/tmp -v ${HOME}/data/ds003_downsampled:/bids_dataset -v ${HOME}/outputs:/outputs bids/${CIRCLE_PROJECT_REPONAME,,} --fast --n_cpus 2 --segmentation_type colin27-subcortical /bids_dataset /outputs participant1 --participant_label 02 :
+    - docker run -ti --rm --read-only -v /tmp:/tmp -v /var/tmp:/var/tmp -v ${HOME}/data/ds003_downsampled:/bids_dataset -v ${HOME}/outputs-colin:/outputs bids/${CIRCLE_PROJECT_REPONAME,,} --n_cpus 2 --segmentation_type colin27-subcortical /bids_dataset /outputs participant1 --participant_label 02 :
         timeout: 2160
-    # participant level tests for a longitudinal dataset
-    - docker run -ti --rm --read-only -v /tmp:/tmp -v /var/tmp:/var/tmp -v ${HOME}/data/ds003_downsampled:/bids_dataset -v ${HOME}/outputs:/outputs bids/${CIRCLE_PROJECT_REPONAME,,} --fast --n_cpus 2 --segmentation_type colin27-subcortical /bids_dataset /outputs participant2 --participant_label 01 :
+    # participant level tests for a longitudinal dataset no masking
+    - docker run -ti --rm --read-only -v /tmp:/tmp -v /var/tmp:/var/tmp -v ${HOME}/data/ds003_downsampled:/bids_dataset -v ${HOME}/outputs-colin:/outputs bids/${CIRCLE_PROJECT_REPONAME,,} --n_cpus 2 --segmentation_type colin27-subcortical /bids_dataset /outputs participant2 --participant_label 01 :
         timeout: 21600
-    - docker run -ti --rm --read-only -v /tmp:/tmp -v /var/tmp:/var/tmp -v ${HOME}/data/ds003_downsampled:/bids_dataset -v ${HOME}/outputs:/outputs bids/${CIRCLE_PROJECT_REPONAME,,} --fast --n_cpus 2 --segmentation_type colin27-subcortical /bids_dataset /outputs participant2 --participant_label 02 :
+    - docker run -ti --rm --read-only -v /tmp:/tmp -v /var/tmp:/var/tmp -v ${HOME}/data/ds003_downsampled:/bids_dataset -v ${HOME}/outputs-colin:/outputs bids/${CIRCLE_PROJECT_REPONAME,,} --n_cpus 2 --segmentation_type colin27-subcortical /bids_dataset /outputs participant2 --participant_label 02 :
         timeout: 21600
-    - docker run -ti --rm --read-only -v /tmp:/tmp -v /var/tmp:/var/tmp -v ${HOME}/data/ds003_downsampled:/bids_dataset -v ${HOME}/outputs:/outputs bids/${CIRCLE_PROJECT_REPONAME,,} --fast --n_cpus 2 --segmentation_type colin27-subcortical /bids_dataset /outputs group :
+    # template level run for downsampled dataset with masking
+    - docker run -ti --rm --read-only -v /tmp:/tmp -v /var/tmp:/var/tmp -v ${HOME}/data/ds003_downsampled:/bids_dataset -v ${HOME}/outputs-colin-labelmask:/outputs bids/${CIRCLE_PROJECT_REPONAME,,} --n_cpus 2 --label-masking --segmentation_type colin27-subcortical /bids_dataset /outputs participant1 --participant_label 01 :
+        timeout: 21600
+    - docker run -ti --rm --read-only -v /tmp:/tmp -v /var/tmp:/var/tmp -v ${HOME}/data/ds003_downsampled:/bids_dataset -v ${HOME}/outputs-colin-labelmask:/outputs bids/${CIRCLE_PROJECT_REPONAME,,} --n_cpus 2 --label-masking --segmentation_type colin27-subcortical /bids_dataset /outputs participant1 --participant_label 02 :
+        timeout: 2160
+    # participant level tests for a longitudinal dataset with masking
+    - docker run -ti --rm --read-only -v /tmp:/tmp -v /var/tmp:/var/tmp -v ${HOME}/data/ds003_downsampled:/bids_dataset -v ${HOME}/outputs-colin-labelmask:/outputs bids/${CIRCLE_PROJECT_REPONAME,,} --n_cpus 2 --label-masking --segmentation_type colin27-subcortical /bids_dataset /outputs participant2 --participant_label 01 :
+        timeout: 21600
+    - docker run -ti --rm --read-only -v /tmp:/tmp -v /var/tmp:/var/tmp -v ${HOME}/data/ds003_downsampled:/bids_dataset -v ${HOME}/outputs-colin-labelmask:/outputs bids/${CIRCLE_PROJECT_REPONAME,,} --n_cpus 2 --label-masking --segmentation_type colin27-subcortical /bids_dataset /outputs participant2 --participant_label 02 :
         timeout: 21600
 
 deployment: