Merge pull request #106 from DCAN-Labs/rc-3.2.0

RC 3.2.0
DCAN-Labs · Apr 30, 2024 · 8c7538c · 8c7538c
2 parents 5c2353e + e6bae23
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diff --git a/Dockerfile b/Dockerfile
@@ -49,146 +49,26 @@ RUN apt-get update -qq \
     && rm -rf /var/lib/apt/lists/* \
     && echo "Downloading FSL ..." \
     && mkdir -p /opt/fsl-6.0.5.1 \
-    && curl -fsSL --retry 5 "https://s3.msi.umn.edu/cabinet-fsl-install/fsl-6.0.5.1-centos7_64.tar.gz" \
-    | tar -xz -C /opt/fsl-6.0.5.1 --no-same-owner  --strip-components 1 \
-    --exclude "fsl/config" \
-    --exclude "fsl/data/first" \
-    --exclude "fsl/data/mist" \
-    --exclude "fsl/data/possum" \
-    --exclude "fsl/data/standard/bianca" \
-    --exclude "fsl/data/standard/tissuepriors" \
-    --exclude "fsl/doc" \
-    --exclude "fsl/etc/default_flobs.flobs" \
-    --exclude "fsl/etc/js" \
-    --exclude "fsl/etc/luts" \
-    --exclude "fsl/etc/matlab" \
-    --exclude "fsl/extras" \
-    --exclude "fsl/include" \
-    --exclude "fsl/refdoc" \
-    --exclude "fsl/src" \
-    --exclude "fsl/tcl" \
-    --exclude "fsl/bin/FSLeyes" \
-    --exclude "fsl/bin/probtrackx2_gpu"  \
-    --exclude "fsl/bin/mist"  \
-    --exclude "fsl/bin/eddy_cuda8.0"  \
-    --exclude "fsl/bin/eddy_cuda10.2"  \
-    --exclude "fsl/bin/eddy_cuda9.1"  \
-    --exclude "fsl/bin/melodic"  \
-    --exclude "fsl/bin/eddy_openmp"  \
-    --exclude "fsl/bin/fabber_asl"  \
-    --exclude "fsl/bin/fabber_cest"  \
-    --exclude "fsl/bin/probtrackx2"  \
-    --exclude "fsl/bin/flameo"  \
-    --exclude "fsl/bin/run_mesh_utils"  \
-    --exclude "fsl/bin/fabber_dualecho"  \
-    --exclude "fsl/bin/fabber_dsc"  \
-    --exclude "fsl/bin/fabber_dce"  \
-    --exclude "fsl/bin/fdt_matrix_merge"  \
-    --exclude "fsl/bin/mvntool"  \
-    --exclude "fsl/bin/fabber"  \
-    --exclude "fsl/bin/fabber_dwi"  \
-    --exclude "fsl/bin/fabber_pet"  \
-    --exclude "fsl/bin/fabber_qbold"  \
-    --exclude "fsl/bin/fabber_t1"  \
-    --exclude "fsl/bin/surf_proj"  \
-    --exclude "fsl/bin/surf2surf"  \
-    --exclude "fsl/bin/probtrackx"  \
-    --exclude "fsl/bin/film_gls"  \
-    --exclude "fsl/bin/ftoz"  \
-    --exclude "fsl/bin/ttoz"  \
-    --exclude "fsl/bin/ttologp"  \
-    --exclude "fsl/bin/contrast_mgr"  \
-    --exclude "fsl/bin/qboot"  \
-    --exclude "fsl/bin/cluster"  \
-    --exclude "fsl/bin/xfibres"  \
-    --exclude "fsl/bin/dtifit"  \
-    --exclude "fsl/bin/vecreg"  \
-    --exclude "fsl/binfsl_mvlm"  \
-    --exclude "fsl/bin/new_invwarp"  \
-    --exclude "fsl/bin/mm"  \
-    --exclude "fsl/binfsl_regfilt"  \
-    --exclude "fsl/bin/pvmfit"  \
-    --exclude "fsl/binfsl_sbca"  \
-    --exclude "fsl/binfslsurfacemaths"  \
-    --exclude "fsl/binfsl_glm"  \
-    --exclude "fsl/bin/swe"  \
-    --exclude "fsl/binfsl_schurprod"  \
-    --exclude "fsl/bin/first_utils"  \
-    --exclude "fsl/bin/filmbabe"  \
-    --exclude "fsl/bin/surf2volume"  \
-    --exclude "fsl/bin/first"  \
-    --exclude "fsl/bin/find_the_biggest"  \
-    --exclude "fsl/bin/feat_model"  \
-    --exclude "fsl/bin/tsplot"  \
-    --exclude "fsl/bin/proj_thresh"  \
-    --exclude "fsl/bin/midtrans"  \
-    --exclude "fsl/binfsl_histogram"  \
-    --exclude "fsl/bin/pulse"  \
-    --exclude "fsl/bin/possum"  \
-    --exclude "fsl/bin/asl_mfree"  \
-    --exclude "fsl/bin/fugue"  \
-    --exclude "fsl/bin/asl_file"  \
-    --exclude "fsl/bin/fast"  \
-    --exclude "fsl/bin/ccops"  \
-    --exclude "fsl/bin/swap_subjectwise"  \
-    --exclude "fsl/bin/swap_voxelwise"  \
-    --exclude "fsl/bin/estimate_metric_distortion"  \
-    --exclude "fsl/bin/prelude"  \
-    --exclude "fsl/bin/betsurf"  \
-    --exclude "fsl/bin/bet2"  \
-    --exclude "fsl/bin/sigloss"  \
-    --exclude "fsl/bin/pointflirt"  \
-    --exclude "fsl/bin/signal2image"  \
-    --exclude "fsl/bin/xfibres_gpu"  \
-    --exclude "fsl/bin/pnm_evs"  \
-    --exclude "fsl/bin/b0calc"  \
-    --exclude "fsl/bin/merge_parts_gpu"  \
-    --exclude "fsl/bin/slicer"  \
-    --exclude "fsl/bin/dtigen"  \
-    --exclude "fsl/bin/drawmesh"  \
-    --exclude "fsl/bin/makerot"  \
-    --exclude "fsl/bin/lesion_filling"  \
-    --exclude "fsl/bin/distancemap"  \
-    --exclude "fsl/bin/overlay"  \
-    --exclude "fsl/bin/prewhiten"  \
-    --exclude "fsl/bin/fdr"  \
-    --exclude "fsl/bin/spharm_rm"  \
-    --exclude "fsl/bin/tbss_skeleton"  \
-    --exclude "fsl/binfslcc"  \
-    --exclude "fsl/bin/slicetimer"  \
-    --exclude "fsl/binfslsmoothfill"  \
-    --exclude "fsl/bin/first_mult_bcorr"  \
-    --exclude "fsl/bin/smoothest"  \
-    --exclude "fsl/bin/calc_grad_perc_dev"  \
-    --exclude "fsl/bin/avscale"  \
-    --exclude "fsl/bin/make_dyadic_vectors"  \
-    --exclude "fsl/binfslpspec"  \
-    --exclude "fsl/bin/susan"  \
-    --exclude "fsl/binfslfft"  \
-    --exclude "fsl/bin/unconfound"  \
-    --exclude "fsl/bin/rmsdiff"  \
-    && find /opt/fsl-6.0.5.1/data/standard -type f -not -name "MNI152_T1_2mm_brain.nii.gz" -delete
+    && curl -sSL "https://s3.msi.umn.edu/bibsnet-data/bibsnet-v3.2.0.tar.gz" \
+    | tar -xzpf - fsl-6.0.5.1-centos7_64.tar.gz -O | tar -xzpC /opt/fsl-6.0.5.1 --no-same-owner --strip-components 1
+
 ENV FSLDIR="/opt/fsl-6.0.5.1" \
-    PATH="/opt/fsl-6.0.5.1/bin:$PATH" \
+    PATH="/opt/afni-latest:/opt/ants:/opt/fsl-6.0.5.1/bin:$PATH" \
     FSLOUTPUTTYPE="NIFTI_GZ" \
     FSLMULTIFILEQUIT="TRUE" \
     FSLLOCKDIR="" \
     FSLMACHINELIST="" \
     FSLREMOTECALL="" \
     FSLGECUDAQ="cuda.q" \
-    LD_LIBRARY_PATH="/opt/fsl-6.0.5.1/lib:$LD_LIBRARY_PATH"
-
-ENV PATH="/opt/afni-latest:$PATH" \
+    LD_LIBRARY_PATH="/opt/fsl-6.0.5.1/lib:$LD_LIBRARY_PATH" \
     AFNI_IMSAVE_WARNINGS="NO" \
     AFNI_PLUGINPATH="/opt/afni-latest"
 
 # Installing ANTs 2.3.3 (NeuroDocker build)
 # Note: the URL says 2.3.4 but it is actually 2.3.3
-ENV ANTSPATH="/opt/ants" \
-    PATH="/opt/ants:$PATH"
-WORKDIR $ANTSPATH
-RUN curl -sSL --retry 5 "https://dl.dropbox.com/s/gwf51ykkk5bifyj/ants-Linux-centos6_x86_64-v2.3.4.tar.gz" \
-    | tar -xzC $ANTSPATH --strip-components 1
+RUN mkdir -p /opt/ants \
+    && curl -sSL --retry 5 "https://dl.dropbox.com/s/gwf51ykkk5bifyj/ants-Linux-centos6_x86_64-v2.3.4.tar.gz" \
+    | tar -xzC /opt/ants --strip-components 1
 
 # Create a shared $HOME directory
 RUN useradd -m -s /bin/bash -G users -u 1000 bibsnet
@@ -205,24 +85,20 @@ RUN cd /home/bibsnet && \
     pip install -e .
 
 #ENV nnUNet_raw_data_base="/output"
-ENV nnUNet_preprocessed="/opt/nnUNet/nnUNet_raw_data_base/nnUNet_preprocessed"
-ENV RESULTS_FOLDER="/opt/nnUNet/nnUNet_raw_data_base/nnUNet_trained_models"
+ENV nnUNet_preprocessed="/opt/nnUNet/nnUNet_raw_data_base/nnUNet_preprocessed" \
+    RESULTS_FOLDER="/opt/nnUNet/nnUNet_raw_data_base/nnUNet_trained_models"
 
 RUN mkdir -p /opt/nnUNet/nnUNet_raw_data_base/ /opt/nnUNet/nnUNet_raw_data_base/nnUNet_preprocessed /opt/nnUNet/nnUNet_raw_data_base/nnUNet_trained_models/nnUNet /home/bibsnet/data
 #COPY trained_models/Task512_BCP_ABCD_Neonates_SynthSegDownsample.zip /opt/nnUNet/nnUNet_raw_data_base/nnUNet_trained_models/nnUNet
-RUN curl -sSL "https://s3.msi.umn.edu/cabinet-data-targz/Task552_uniform_distribution_synthseg.tar.gz" | tar -xzC /opt/nnUNet/nnUNet_raw_data_base/nnUNet_trained_models/nnUNet --no-same-owner --strip-components 1 && \
-    curl -sSL "https://s3.msi.umn.edu/cabinet-data-targz/Task514_BCP_ABCD_Neonates_SynthSeg_T1Only.tar.gz" | tar -xzC /opt/nnUNet/nnUNet_raw_data_base/nnUNet_trained_models/nnUNet --no-same-owner --strip-components 1 && \
-    curl -sSL "https://s3.msi.umn.edu/cabinet-data-targz/Task515_BCP_ABCD_Neonates_SynthSeg_T2Only.tar.gz" | tar -xzC /opt/nnUNet/nnUNet_raw_data_base/nnUNet_trained_models/nnUNet --no-same-owner --strip-components 1
-
-RUN curl -sSL "https://dl.dropbox.com/s/gwf51ykkk5bifyj/ants-Linux-centos6_x86_64-v2.3.4.tar.gz" \
-    | tar -xzC $ANTSPATH --strip-components 1
+RUN curl -sSL "https://s3.msi.umn.edu/bibsnet-data/bibsnet-v3.2.0.tar.gz" | tar -xzpf - Task552_uniform_distribution_synthseg.tar.gz -O | tar -xzpC /opt/nnUNet/nnUNet_raw_data_base/nnUNet_trained_models/nnUNet --no-same-owner --strip-components 1 && \
+    curl -sSL "https://s3.msi.umn.edu/bibsnet-data/bibsnet-v3.2.0.tar.gz" | tar -xzpf - Task514_BCP_ABCD_Neonates_SynthSeg_T1Only.tar.gz -O | tar -xzpC /opt/nnUNet/nnUNet_raw_data_base/nnUNet_trained_models/nnUNet --no-same-owner --strip-components 1 &&\
+    curl -sSL "https://s3.msi.umn.edu/bibsnet-data/bibsnet-v3.2.0.tar.gz" | tar -xzpf - Task515_BCP_ABCD_Neonates_SynthSeg_T2Only.tar.gz -O | tar -xzpC /opt/nnUNet/nnUNet_raw_data_base/nnUNet_trained_models/nnUNet --no-same-owner --strip-components 1 && \
+    curl -sSL "https://s3.msi.umn.edu/bibsnet-data/bibsnet-v3.2.0.tar.gz" | tar -xzpf - Task526_BIBSNet_Production_Model.tar.gz -O | tar -xzpC /opt/nnUNet/nnUNet_raw_data_base/nnUNet_trained_models/nnUNet --no-same-owner --strip-components 1
 
 COPY run.py /home/bibsnet/run.py
 COPY src /home/bibsnet/src
-RUN chmod 777 -R /opt/fsl-6.0.5.1
-RUN bash /home/bibsnet/src/fixpy.sh /opt/fsl-6.0.5.1
 COPY bin /home/bibsnet/bin
-RUN curl -sSL "https://s3.msi.umn.edu/cabinet-data-targz/data.tar.gz" | tar -xzC /home/bibsnet/data --no-same-owner --strip-components 1
+RUN curl -sSL "https://s3.msi.umn.edu/bibsnet-data/bibsnet-v3.2.0.tar.gz" | tar -xzpf - data.tar.gz -O | tar -xzpC /home/bibsnet/data --no-same-owner --strip-components 1
 
 COPY requirements.txt  /home/bibsnet/requirements.txt
 
@@ -231,8 +107,6 @@ ENV PATH="${PATH}:/home/bibsnet/"
 RUN cp /home/bibsnet/run.py /home/bibsnet/bibsnet
 
 RUN cd /home/bibsnet/ && pip install -r requirements.txt
-RUN cd /home/bibsnet/ && chmod 555 -R run.py bin src bibsnet data
-RUN chmod -R a+r /opt/nnUNet/nnUNet_raw_data_base/nnUNet_trained_models/nnUNet/3d_fullres
-RUN find /opt/nnUNet/nnUNet_raw_data_base/nnUNet_trained_models/nnUNet/3d_fullres -type f -name 'postprocessing.json' -exec chmod 666 {} \;
+RUN cd /home/bibsnet/ && chmod 555 -R run.py bin src bibsnet
 
 ENTRYPOINT ["bibsnet"]
diff --git a/data/dataset_description.json b/data/dataset_description.json
diff --git a/data/models.csv b/data/models.csv
diff --git a/data/sidecar_template.json b/data/sidecar_template.json
diff --git a/docs/comingsoon.md b/docs/comingsoon.md
diff --git a/docs/stages.md b/docs/stages.md
@@ -19,9 +19,10 @@ Quickly and accurately segments an optimally-aligned T1 and T2 pair with a deep
 
 | Model | Description |
 |:-:|:--|
-| 552 | Default T1w and T2w model |
 | 514 | Default T1w-only model |
 | 515 | Default T2w-only model |
+| 526 | Default T1w and T2w model |
+| 552 | Old T1w and T2w model |
 
 <br />
 

diff --git a/docs/usage.md b/docs/usage.md
@@ -10,9 +10,9 @@ usage: BIBSnet [-h] [-participant PARTICIPANT_LABEL] [-age AGE_MONTHS]
                [--fsl-bin-path FSL_BIN_PATH] [-jargs PARAMETER_JSON]
                [-model MODEL] [--nnUNet NNUNET]
                [--nnUNet-configuration {2d,3d_fullres,3d_lowres,3d_cascade_fullres}]
-               [--overwrite] [-ses SESSION]
-               [-start {prebibsnet,bibsnet,postbibsnet}] [-w WORK_DIR] [-z]
-               [--script-dir SCRIPT_DIR] [-v | -d]
+               [--overwrite] [--reduce-cropping [REDUCE_CROPPING]]
+               [-ses SESSION] [-start {prebibsnet,bibsnet,postbibsnet}]
+               [-w WORK_DIR] [-z] [-v | -d]
                bids_dir output_dir {participant}
 
 positional arguments:
@@ -55,7 +55,7 @@ optional arguments:
                         be inferred from /home/bibsnet/data/models.csv based
                         on which data exists in the --bids-dir. BIBSnet will
                         run model 514 by default for T1w-only, model 515 for
-                        T2w-only, and model 552 for both T1w and T2w.
+                        T2w-only, and model 526 for both T1w and T2w.
   --nnUNet NNUNET, -n NNUNET
                         Valid path to existing executable file to run nnU-
                         Net_predict. By default, this script will assume that
@@ -69,6 +69,22 @@ optional arguments:
                         by default BIBSnet will skip creating any BIBSnet
                         output files that already exist in the sub-directories
                         of derivatives.
+  --reduce-cropping [REDUCE_CROPPING]
+                        This flag is used to specify a value by which to
+                        increase or decrease the brain z size used by FSL
+                        robustfov for cropping. This is useful for cases where
+                        the default age-specific brain z size specified by
+                        BIBSNet (calculated based on a table within the
+                        container of BCP participants' average head radius per
+                        age: data/age_to_avg_head_radius_BCP.csv) results in
+                        overcropping. The brain z size can be adjusted by a
+                        specified amount by including an integer with this
+                        flag [REDUCE_CROPPING]: positive integers will
+                        increase brain z size to crop less and negative
+                        integers will decrease brain z size to crop more.
+                        Default: Include this flag by itself to increase the
+                        brain z size and therefore reduce cropping by 20
+                        millimeters.
   -ses SESSION, --session SESSION, --session-id SESSION
                         The name of the session to processes participant data
                         for. Example: baseline_year1
@@ -84,10 +100,6 @@ optional arguments:
                         brain_z_size column.Otherwise, BIBSnet will estimate
                         the brain height from the participant age and averages
                         of a large sample of infant brain heights.
-  --script-dir SCRIPT_DIR
-                        Valid path to the existing parent directory of this
-                        run.py script. Include this argument if and only if
-                        you are running the script as a SLURM/SBATCH job.
   -v, --verbose         Include this flag to print detailed information and
                         every command being run by BIBSnet to stdout.
                         Otherwise BIBSnet will only print warnings, errors,
@@ -129,12 +141,17 @@ This has been primarily tested in Singularity. We are less able to provide techn
 We do not recommend running `BIBSnet` outside of the container for the following reasons:
 
 1. Installing nnU-Net can be complicated.
+
 1. Running `BIBSnet` inside the container ensures you have the proper versions of all software.
+
 1. It is hard to diagnose your errors if you are working in a different environment.
 
 However, if you run `BIBSnet` outside of the container as an application, then you will need to do the following:
 
-1. Download the `data` directory from the `https://s3.msi.umn.edu/CABINET_data/data.zip` URL, unzip it, and move it into your cloned `BIBSnet` repository directory here: `BIBSnet/data/`
+1. Download the appropriate data release from `https://s3.msi.umn.edu/bibsnet-data/<DATA_RELEASE>.tar.gz`
+
+1. Extract `data.tar.gz` then extract all files in it and move them into your cloned `BIBSnet` repository directory here: `BIBSnet/data/`
+
 1. Install [nnU-Net](https://github.com/MIC-DKFZ/nnUNet#installation)
 
 <br />
diff --git a/mkdocs.yml b/mkdocs.yml
@@ -5,11 +5,10 @@ plugins:
 docs_dir: docs
 nav:
   - 1. Overview: index.md
-  - 2. Coming Soon: comingsoon.md
-  - 3. Installation: installation.md
-  - 4. Usage: usage.md
-  - 5. Participant and Sessions TSV Requirements: participants.md
-  - 6. Stages: stages.md
-  - 7. Outputs: outputs.md
-  - 8. Contributions: contributions.md
+  - 2. Installation: installation.md
+  - 3. Usage: usage.md
+  - 4. Participant and Sessions TSV Requirements: participants.md
+  - 5. Stages: stages.md
+  - 6. Outputs: outputs.md
+  - 7. Contributions: contributions.md
 
diff --git a/requirements.txt b/requirements.txt
@@ -1,5 +1,5 @@
 nibabel==3.2.1
 nipype==1.8.6
 pandas==1.3.5
-scipy==1.8.0
+scipy==1.10.0
 niworkflows==1.9.0
diff --git a/src/fixpy.sh b/src/fixpy.sh