![]() Probably 4-6 landmarks are enough, but they need to be distributed over the head (not just on the face, but on the posterior side, too). Up to a few mm error is acceptable (since displacement of the cut by even as much as 5-10 millimeters, would probably cause just a few percent surface area measurement error). Register the pre-op and post-op CT using Fiducial registration wizard module.Segment the skull on the pre-op and post-op image and export them to model nodes.Is there any way for better alignment? Is it better to align the raw data from CT scans and then create the model? Maybe a script with python making tests with alignment scenarios and finding out the best could be a solution? (I have experience with vb.net I am a “noob” in terms of python, and I don’t know how easily this could be achieved)īased on what you describe the following will provide high enough accuracy and does not require any custom development, and can be completed in a few minutes per case (by practicing or Python-scripting the repetitive steps): Finally, I need a balance between time and accuracy, but I would prefer a greater accuracy method. This method would be the most scientific of all.Īs you figured out, the problems are the alignment that affects significantly accuracy and the long time needed to create the final model.Īlso, I would like to know or calculate the extend of error because of the software itself. The third is to compare the two CT scans and create the model with subtraction. (in this case, only one ct scan will be needed. The second one is to create a model by comparing it with the other healthy side of the skull, like building a mirror image. The first one is to create a flat model without taking into account the curvature. This area is irregular and has a curvature. What I want to do is to calculate the area of this bone defect. The surgical operation is called decompressive craniectomy and consists of removing a large piece o f bone out of the skull. I have one CT scan before the operation and one after the procedure. I have a pair of CT images for every patient (around 100 patients, a number which may be doubled). As usual release notes are here in the wiki.Let me explain to you want I want to do. * And obviously a lot of small bug issues. Full texture parametrization of meshes ahead in the next version. you can create an abstract texture and then use it to remesh your model in a very nice way. The current version of the filter support only the remeshing side of the technique, e.g. * The new abstract surface parametrization algorithm in now inside MeshLab currently it is a bit slow and buggy (well it is the first release) so sometime it can crash. Of point cloud data and to limit the attribute transfer to a limitedĭistance. * Improved the vertex attribute transfer filter (the filter that allows you to transfer color, vertex, position, quality from a mesh to another one) to support the management simplify more the internal regions, preserve better the face of a character, etc, etc.). * Weighted simplification you can now weight the simplification process with a generic scalar value (e.g. It feature various ways of building meshes from pdb description. This time a lot of large internal changes (we redesigned the parameter mechanism of the filters for a better previewing mechanism) and we added a few new features: * pdb molecular importing to build up meshes from molecular description.
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