Package: resseg
RESSEGAutomatic segmentation of postoperative brain resection cavities from magnetic resonance images (MRI) using a convolutional neural network (CNN) trained withPyTorch1.7.1.InstallationIt's recommended to useconda.A 6-GB GPU is large enough to segment an image in an MNI space of size 193 × 229 × 193.condacreate-nressegpython=3.8-y
condaactivateresseg
pipinstalllight-the-torch
lttinstalltorch
pipinstallresseg
resseg--helpUsageBelow are two examples of cavity segmentation for tumor and epilepsy surgery. The epilepsy example includes registration to theMNI space.
Both examples can be run online using Google Colab:BITEExample using an image from theBrain Images of Tumors for Evaluation database (BITE).BITE=`resseg-downloadbite`resseg$BITE-obite_seg.nii.gzEPISURGExample using an image from theEPISURG dataset.
Segmentation works best when images are in the MNI space, soressegincludes a tool
for this purpose (requires`antspyx).pipinstallantspyxEPISURG=`resseg-downloadepisurg`resseg-mni$EPISURG-tepisurg_to_mni.tfm
resseg$EPISURG-oepisurg_seg.nii.gz-tepisurg_to_mni.tfmTrained modelThe trained model can be used without installingresseg, but you'll need to installunetfirst:pipinstallunet==0.7.7Then, in Python:importtorchrepo='fepegar/resseg'model_name='ressegnet'model=torch.hub.load(repo,model_name,pretrained=True)Graphical user interface using 3D SlicerThere is an experimental graphical user interface (GUI) built on top of3D Slicer.Visitthis repositoryfor additional information and installation instructions.Plotting resected structuresA quantitative analysis of the resected structures can be performed using a brain parcellation computed usingGIF(3.0) orFreeSurfer.fromresseg.parcellationimportGIFParcellation,FreeSurferParcellationparcellation_path='t1_seg_gif.nii.gz'cavity_seg_on_preop_path='cavity_on_preop.nii.gz'parcellation=GIFParcellation(parcellation_path)I used a sphere near the hippocampus to simulate the resection cavity segmentation, and the GIF parcellation in theFPG datasetofTorchIO.parcellation.print_percentage_of_resected_structures(cavity_seg_on_preop_path)Percentage of each resected structure:
100% of Left vessel
 83% of Left Inf Lat Vent
 59% of Left Amygdala
 58% of Left Hippocampus
 26% of Left PIns posterior insula
 24% of Left PP planum polare
 21% of Left Basal Forebrain
 18% of Left Claustrum
 16% of Left PHG parahippocampal gyrus
 15% of Left Pallidum
 15% of Left Ent entorhinal area
 13% of Left FuG fusiform gyrus
 13% of Left Temporal White Matter
 11% of Left Putamen
 10% of Left Insula White Matter
  5% of Left ITG inferior temporal gyrus
  5% of Left periventricular white matter
  5% of Left Ventral DC

The resection volume is composed of:
 30% is Left Temporal White Matter
 12% is Left Hippocampus
 10% is Left Insula White Matter
  7% is Left FuG fusiform gyrus
  6% is Left Amygdala
  4% is Left ITG inferior temporal gyrus
  4% is Left PP planum polare
  3% is Left Putamen
  3% is Left Claustrum
  3% is Left PIns posterior insula
  3% is Left PHG parahippocampal gyrus
  2% is [Unkown label: 4]
  1% is Left Ent entorhinal area
  1% is Left Pallidum
  1% is Left Inf Lat Vent
  1% is Left Ventral DCparcellation.plot_bars(cavity_seg_on_preop_path)parcellation.plot_pie(cavity_seg_on_preop_path)CreditIf you use this library for your research, please cite the following publications:F. Pérez-García et al., 2020,Simulation of Brain Resection for Cavity Segmentation Using Self-Supervised and Semi-Supervised Learning.F. Pérez-García et al., 2021,A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections.If you use theEPISURG dataset, which was used to train the model, please cite the following publication:F. Pérez-García et al., 2020,EPISURG: a dataset of postoperative magnetic resonance images (MRI) for quantitative analysis of resection neurosurgery for refractory epilepsy. University College London. Dataset.See alsoresectorwas used to simulate brain resections during trainingTorchIOwas also used extensively. Bothressegandresectorrequire this library.