The latest release includes a new function to predict the landmarks needed to place the 10-10-system on the skin: Predict1010SystemLandmarks
The landmarks are the nasion, inion, and left/right pre-auricular points. Sim4Life now can predict these directly from a T1w MRI.

The following script demonstrates the whole process:

from ImageML import Predict1010SystemLandmarks from s4l_v1.model import Vec3, Import, Create1010System, PlaceElectrodes, CreateSolidCylinder from s4l_v1.model.image import HeadModelGeneration, ExtractSurface img = Import(r"D:\datasets\IXI-T1\IXI021-Guys-0703-T1.nii.gz")[0] # segment head, skip adding dura, labelfield = HeadModelGeneration([img], output_spacing=0.6, add_dura=False) # extract surfaces from segmentation surfaces = ExtractSurface(labelfield) surfaces_dict = {e.Name: e for e in surfaces} skin = surfaces_dict["Skin"] # predict landmarks, the function returns a list of Vertex entities verts = Predict1010SystemLandmarks(img) pts = {e.Name: e.Position for e in verts} eeg1010_group = Create1010System(skin, Nz=pts["Nz"], Iz=pts["Iz"], RPA=pts["RPA"], LPA=pts["LPA"]) eeg1010_dict = {e.Name: e for e in eeg1010_group.Entities} # create template electrode and place it at C3 position electrode_template = CreateSolidCylinder(Vec3(0), Vec3(0,0,5), radius=10) electrodes = PlaceElectrodes([electrode_template], [eeg1010_dict["C3"]])

For the image used in this example, the result looks like this:

4b86e97a-5c74-4e98-8b56-386c0b967ecf-image.png

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