NnU-Net versus mesh growing algorithm as a tool for the robust and timely segmentation of neurosurgical 3D images in contrast-enhanced T1 MRI scans

Authors:
raw_author_name: Tessa M. Kos
raw_affiliation_string: Image Sciences Institute, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.
raw_author_name: Tim Fick
raw_affiliation_string: Department of Neuro-Oncology, Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.
raw_author_name: Jesse A. M. van Doormaal
raw_affiliation_string: Department of Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands.
raw_author_name: Elisa Colombo
raw_affiliation_string: Department of Neurosurgery, University Hospital of Zürich, Zurich, Switzerland.
raw_author_name: Hugo J. Kuijf
raw_affiliation_string: Image Sciences Institute, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.
raw_author_name: Pierre A. J. T. Robe
raw_affiliation_string: Department of Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands.
raw_author_name: Luca P. Regli
raw_affiliation_string: Department of Neurosurgery, University Hospital of Zürich, Zurich, Switzerland.
raw_author_name: Lambertus W. Bartels
raw_affiliation_string: Image Sciences Institute, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.
raw_author_name: Tristan P. C. van Doormaal
raw_affiliation_string: Department of Neurosurgery, University Hospital of Zürich, Zurich, Switzerland.; Department of Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands.

Abstract: Abstract Purpose This study evaluates the nnU-Net for segmenting brain, skin, tumors, and ventricles in contrast-enhanced T1 (T1CE) images, benchmarking it against an established mesh growing algorithm (MGA). Methods We used 67 retrospectively collected annotated single-center T1CE brain scans for training models for brain, skin, tumor, and ventricle segmentation. An additional 32 scans from two centers were used test performance compared to that of the MGA. The performance was measured using the Dice-Sørensen coefficient (DSC), intersection over union (IoU), 95th percentile Hausdorff distance (HD95), and average symmetric surface distance (ASSD) metrics, with time to segment also compared. Results The nnU-Net models significantly outperformed the MGA ( p &lt; 0.0125) with a median brain segmentation DSC of 0.971 [95CI: 0.945–0.979], skin: 0.997 [95CI: 0.984–0.999], tumor: 0.926 [95CI: 0.508–0.968], and ventricles: 0.910 [95CI: 0.812–0.968]. Compared to the MGA’s median DSC for brain: 0.936 [95CI: 0.890, 0.958], skin: 0.991 [95CI: 0.964, 0.996], tumor: 0.723 [95CI: 0.000–0.926], and ventricles: 0.856 [95CI: 0.216–0.916]. NnU-Net performance between centers did not significantly differ except for the skin segmentations Additionally, the nnU-Net models were faster (mean: 1139 s [95CI: 685.0–1616]) than the MGA (mean: 2851 s [95CI: 1482–6246]). Conclusions The nnU-Net is a fast, reliable tool for creating automatic deep learning-based segmentation pipelines, reducing the need for extensive manual tuning and iteration. The models are able to achieve this performance despite a modestly sized training set. The ability to create high-quality segmentations in a short timespan can prove invaluable in neurosurgical settings.