Fiducial Finder Initial Steps

[hjmjohnson]

User Story

As an AlgorithmDev ,
I want understand the current field of landmark identification from deep learning methods
so that anatomical landmarks may be identified

Detailed Description

Survey literature to identify potential landmark identification strategies.

Tasks to be completed

• Survey different approaches to deep learning
• Provide a proposal for initial testing (what will be tested, how will it be evaluated)
• Provide small scale prototype

Acceptance Criteria

• Create a wiki page to report on the literature findings (https://github.com/BRAINSia/SINAPSE/wiki/Fiducial-Finder)
• Write wiki page proposal of first experiment
• Write a wiki page report of initial small scale prototype results

[hjmjohnson]

cat requirements.txt
alabaster==0.7.12
appdirs==1.4.3
appnope==0.1.0
attrs==19.1.0
Babel==2.6.0
backcall==0.1.0
bids-validator==1.2.3
black==19.3b0
bleach==3.1.0
certifi==2019.3.9
chardet==3.0.4
Click==7.0
cycler==0.10.0
decorator==4.4.0
defusedxml==0.6.0
docopt==0.6.2
docutils==0.14
entrypoints==0.3
idna==2.8
imagesize==1.1.0
ipydatawidgets==4.0.0
ipykernel==5.1.0
ipympl==0.2.1
ipython==7.5.0
ipython-genutils==0.2.0
ipywidgets==7.4.2
itk==5.0rc1
itk-core==5.0rc2
itk-filtering==5.0rc2
itk-io==5.0rc2
itk-numerics==5.0rc2
itk-registration==5.0rc2
itk-segmentation==5.0rc2
itkwidgets==0.16.1
jedi==0.13.3
Jinja2==2.10.1
jsonschema==3.0.1
jupyter-client==5.2.4
jupyter-core==4.4.0
jupyterlab==0.35.6
jupyterlab-server==0.2.0
kiwisolver==1.1.0
MarkupSafe==1.1.1
matplotlib==3.0.3
mistune==0.8.4
nbconvert==5.5.0
nbformat==4.4.0
nibabel==2.4.0
notebook==5.7.8
num2words==0.5.9
numpy==1.16.3
packaging==19.0
pandas==0.24.2
pandocfilters==1.4.2
parso==0.4.0
patsy==0.5.1
pexpect==4.7.0
pickleshare==0.7.5
prometheus-client==0.6.0
prompt-toolkit==2.0.9
ptyprocess==0.6.0
pybids==0.8.0+77.g30d924c
Pygments==2.3.1
pyparsing==2.4.0
pyrsistent==0.15.1
python-dateutil==2.8.0
pytz==2019.1
pyzmq==18.0.1
requests==2.21.0
scikit-learn==0.20.3
scipy==1.2.1
Send2Trash==1.5.0
SimpleITK==1.2.0
six==1.12.0
snowballstemmer==1.2.1
Sphinx==2.0.1
sphinx-rtd-theme==0.4.3
sphinxcontrib-applehelp==1.0.1
sphinxcontrib-devhelp==1.0.1
sphinxcontrib-htmlhelp==1.0.2
sphinxcontrib-jsmath==1.0.1
sphinxcontrib-qthelp==1.0.2
sphinxcontrib-serializinghtml==1.1.3
terminado==0.8.2
testpath==0.4.2
toml==0.10.0
tornado==6.0.2
traitlets==4.3.2
traittypes==0.2.1
urllib3==1.24.3
wcwidth==0.1.7
webencodings==0.5.1
widgetsnbextension==3.4.2
zstandard==0.11.0

def read_fiducial_points(fcsv_file: str, fiducials:list) -> dict:
    col_names="id,x,y,z,ow,ox,oy,oz,vis,sel,lock,label,desc,associatedNodeID"
    with open(fcsv_file, 'r') as fid: 
        df = pd.read_csv(fid, sep=',', comment='#', names=col_names.split(',')) 

    fid_pts = {}
    for fid in fiducials:
        fid_pts[fid] = df[ df['label'] == fid].iloc[0][1:4].values
        # flip RAS fiducials to be LPS
        fid_pts[fid][0] = -1*fid_pts[fid][0]
        fid_pts[fid][1] = -1*fid_pts[fid][1]
    return fid_pts