Virtual brain grafting: Enabling whole brain parcellation in the presence of large lesions

Ahmed Radwan (Corresponding author), Louise Emsell, Jeroen Blommaert, Andrey Zhylka, Sylvia Kovacs, Tom Theys, Nico Sollmann, Patrick Dupont, Stefan Sunaert

Research output: Contribution to journalArticleAcademicpeer-review

21 Citations (Scopus)


Brain atlases and templates are at the heart of neuroimaging analyses, for which they facilitate multimodal registration, enable group comparisons and provide anatomical reference. However, as atlas-based approaches rely on correspondence mapping between images they perform poorly in the presence of structural pathology. Whilst several strategies exist to overcome this problem, their performance is often dependent on the type, size and homogeneity of any lesions present. We therefore propose a new solution, referred to as Virtual Brain Grafting (VBG), which is a fully-automated, open-source workflow to reliably parcellate magnetic resonance imaging (MRI) datasets in the presence of a broad spectrum of focal brain pathologies, including large, bilateral, intra- and extra-axial, heterogeneous lesions with and without mass effect. The core of the VBG approach is the generation of a lesion-free T1-weighted input image which enables further image processing operations that would otherwise fail. Here we validated our solution based on Freesurfer recon-all parcellation in a group of 10 patients with heterogeneous gliomatous lesions, and a realistic synthetic cohort of glioma patients (n=100) derived from healthy control data and patient data. We demonstrate that VBG outperforms a non-VBG approach assessed qualitatively by expert neuroradiologists and Mann-Whitney U tests to compare corresponding parcellations (real patients U(6,6) = 33, z = 2.738, P < .010, synthetic-patients U(48,48) = 2076, z = 7.336, P < .001). Results were also quantitatively evaluated by comparing mean dice scores from the synthetic-patients using one-way ANOVA (unilateral VBG = 0.894, bilateral VBG = 0.903, and non-VBG = 0.617, P < .001). Additionally, we used linear regression to show the influence of lesion volume, lesion overlap with, and distance from the Freesurfer volumes of interest, on labelling accuracy. VBG may benefit the neuroimaging community by enabling automated state-of-the-art MRI analyses in clinical populations using methods such as FreeSurfer, CAT12, SPM, Connectome Workbench, as well as structural and functional connectomics. To fully maximize its availability, VBG is provided as open software under a Mozilla 2.0 license ( Graphical abstract Image, graphical abstract Download : Download high-res image (208KB)Download : Download full-size image (A) shows T1 images from two patients with gliomatous lesions. VBG is a lesion replacement/filling workflow with one approach for unilateral lesions (uVBG) and one for bilateral lesion (bVBG). (B) shows the lesion filling and recon-all combination selected, (C) & (D) show the output, tissue segmentations (C) and whole brain parcellations (D). If VBG is not used (non-VBG) recon-all may quit without generating a parcellation (hard failure) shown on the lower left, or finish with some errors (soft failures) in the parcellations shown on the lower right. However, using either VBG method allows recon-all to complete where it had previously failed and also improves parcellation quality. (PAT = patient, VBG = virtual brain grafting, uVBG = unilateral VBG, bVBG = bilateral VBG)
Original languageEnglish
Article number117731
Number of pages13
Publication statusPublished - 1 Apr 2021


  • Brain MRI lesion-filling
  • Brain MRI lesion-inpainting
  • Clinical imaging
  • Gliomas
  • Lesioned brain parcellation


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