Efficient Brain Extraction of MRI Scans with Mild to Moderate Neuropathology

Hjalti Thrastarson; Lotta M. Ellingsen

arxiv: 2602.08764 · v1 · submitted 2026-02-09 · 📡 eess.IV · cs.AI· cs.CV

Efficient Brain Extraction of MRI Scans with Mild to Moderate Neuropathology

Hjalti Thrastarson , Lotta M. Ellingsen This is my paper

Pith reviewed 2026-05-16 05:32 UTC · model grok-4.3

classification 📡 eess.IV cs.AIcs.CV

keywords skull strippingbrain extractionMRIU-netsigned distance transformneuropathologyT1-weightedsulcal CSF

0 comments

The pith

A modified U-net trained with signed-distance loss produces consistent brain masks from T1 MRI that include sulcal fluid but exclude meninges.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a skull-stripping method for T1-weighted MRI that remains reliable even when mild or moderate neuropathology is present. It trains a U-net variant on silver-standard labels using a loss derived from the signed distance transform to encourage precise surface placement. This matters because many clinical and research pipelines begin with brain extraction, and failures here propagate to all later steps such as structure segmentation or volume measurement. The resulting masks are designed to follow the outer cortical surface including sulci while leaving out the full subarachnoid space and meninges, producing more uniform boundaries than previous tools. Validation on held-out and external data shows the approach yields consistent overlap and surface distances.

Core claim

The authors present a U-net architecture modified for skull stripping and trained with a novel signed-distance-transform loss on silver-standard ground truth. The method is shown to segment the outer brain surface consistently, including sulcal cerebrospinal fluid but excluding the full subarachnoid space and meninges, while operating efficiently on T1-weighted images that contain mild to moderate neuropathology.

What carries the argument

A modified U-net whose training loss is based on the signed-distance transform of the target brain mask; this loss penalizes deviations from the desired surface location and enables the network to learn a consistent boundary definition.

If this is right

Downstream automatic segmentation of brain structures becomes more reliable because the input masks have consistent outer boundaries.
Longitudinal studies can track brain changes with less variability introduced by the extraction step.
The method handles mild to moderate pathology without the failures common in intensity-based or atlas-based strippers.
Public release allows immediate integration into existing MRI processing pipelines.
Performance remains high on independent external data, suggesting good generalization.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The boundary definition that includes sulcal CSF but excludes meninges may align better with some volumetric studies of cortical thickness.
Retraining the same network on different silver-standard sets could adapt the method to alternative boundary conventions without changing the architecture.
Extension to multi-modal inputs or 3D convolutions might further improve accuracy on severe pathology cases.
Comparison against manual expert delineations that explicitly exclude the subarachnoid space would provide a cleaner performance benchmark.

Load-bearing premise

The silver-standard ground truth used for training accurately captures the intended brain boundary that includes sulcal CSF but excludes the meninges and full subarachnoid space.

What would settle it

A set of MRI scans with expert manual brain masks that strictly exclude the subarachnoid space and meninges; if the model's Dice coefficient on this set falls below 0.90 or the surface distance exceeds 3 mm, the claim of consistent and accurate extraction would be undermined.

Figures

Figures reproduced from arXiv: 2602.08764 by Hjalti Thrastarson, Lotta M. Ellingsen.

**Figure 1.** Figure 1: The architecture of the proposed model. The numbers in the blocks signify the number of channels it outputs. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

**Figure 2.** Figure 2: Automatic brain segmentation of an image with movement artefacts. Methods are (a) MONSTR, (b) ROBEX, [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Figure 3: Automatic brain segmentation of a standard T1-weighted MRI from a healthy subject. Methods are (a) MON [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

**Figure 4.** Figure 4: Automatic brain segmentation of a sample from the IXI dataset. Methods are (a) silver-standard ground truth, [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

read the original abstract

Skull stripping magnetic resonance images (MRI) of the human brain is an important process in many image processing techniques, such as automatic segmentation of brain structures. Numerous methods have been developed to perform this task, however, they often fail in the presence of neuropathology and can be inconsistent in defining the boundary of the brain mask. Here, we propose a novel approach to skull strip T1-weighted images in a robust and efficient manner, aiming to consistently segment the outer surface of the brain, including the sulcal cerebrospinal fluid (CSF), while excluding the full extent of the subarachnoid space and meninges. We train a modified version of the U-net on silver-standard ground truth data using a novel loss function based on the signed-distance transform (SDT). We validate our model both qualitatively and quantitatively using held-out data from the training dataset, as well as an independent external dataset. The brain masks used for evaluation partially or fully include the subarachnoid space, which may introduce bias into the comparison; nonetheless, our model demonstrates strong performance on the held-out test data, achieving a consistent mean Dice similarity coefficient (DSC) of 0.964$\pm$0.006 and an average symmetric surface distance (ASSD) of 1.4mm$\pm$0.2mm. Performance on the external dataset is comparable, with a DSC of 0.958$\pm$0.006 and an ASSD of 1.7$\pm$0.2mm. Our method achieves performance comparable to or better than existing state-of-the-art methods for brain extraction, particularly in its highly consistent preservation of the brain's outer surface. The method is publicly available on GitHub.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

A practical U-net skull-stripping tool with a signed-distance loss and an honest flag on evaluation mismatch, but the performance numbers need that caveat to be taken seriously.

read the letter

The main thing here is a modified U-net trained with a signed-distance-transform loss to extract brains from T1 MRIs that have mild to moderate neuropathology. The target boundary includes sulcal CSF but cuts out the full subarachnoid space and meninges, and the model posts Dice scores around 0.96 on held-out and external sets with code released on GitHub. That is the core deliverable: a consistent, open preprocessing step where many standard tools break down on pathology cases. The signed-distance loss and the exact boundary choice are the actual new pieces; they are not just another U-net but a deliberate way to push the network toward surface consistency. The paper does well by calling out the evaluation issue in the abstract itself, reporting error bars, and keeping the method simple enough to reproduce. The soft spot is the one the stress-test note highlights. Because the reference masks used for scoring include more subarachnoid space than the model is trained to exclude, the Dice and surface-distance numbers do not cleanly measure how well the outer surface is recovered against a matching definition. Any claim of being comparable or better than existing methods therefore rests on an apples-to-oranges comparison, even if the absolute numbers look good. The authors are transparent about this, which keeps it from being a hidden flaw, but it still limits how much weight the superiority statements can carry without follow-up checks on aligned boundaries. This is for people running MRI pipelines who need a reliable skull-stripping step on pathological scans before they move to segmentation or quantification. A reader who wants an open tool to test on their own data would get immediate practical value. I would send it to peer review. The method is clear, the code is public, and the central limitation is already stated, so referees can focus on whether the boundary choice holds up in practice rather than on hidden problems.

Referee Report

1 major / 0 minor

Summary. The paper proposes a modified U-Net for skull-stripping T1-weighted MRI scans in cases of mild to moderate neuropathology. Trained on silver-standard data with a signed-distance-transform loss, the model targets inclusion of sulcal CSF while excluding the full subarachnoid space and meninges. It reports strong quantitative results on held-out test data (mean DSC 0.964±0.006, ASSD 1.4±0.2 mm) and an external dataset (DSC 0.958±0.006, ASSD 1.7±0.2 mm), claiming performance comparable or better than existing SOTA methods with high consistency in outer-surface preservation. The code is made publicly available.

Significance. If the boundary-definition mismatch is resolved, the work would provide a reproducible, efficient tool for consistent brain extraction that handles neuropathology better than many prior methods, supporting more reliable downstream tasks such as structure segmentation in clinical imaging pipelines.

major comments (1)

[Abstract] Abstract: The headline DSC and ASSD values are computed exclusively against silver-standard and external ground-truth masks that 'partially or fully include the subarachnoid space'. The model is trained to exclude the full extent of the subarachnoid space and meninges (while including sulcal CSF). Because the evaluation boundary does not match the training target, the reported metrics cannot be interpreted as direct evidence of accurate outer-surface preservation; any claim of superiority over SOTA may be an artifact of this mismatch rather than a genuine improvement in boundary consistency.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback and for identifying the important issue of boundary mismatch between training and evaluation. We address this point directly below and agree that clarification is warranted.

read point-by-point responses

Referee: [Abstract] Abstract: The headline DSC and ASSD values are computed exclusively against silver-standard and external ground-truth masks that 'partially or fully include the subarachnoid space'. The model is trained to exclude the full extent of the subarachnoid space and meninges (while including sulcal CSF). Because the evaluation boundary does not match the training target, the reported metrics cannot be interpreted as direct evidence of accurate outer-surface preservation; any claim of superiority over SOTA may be an artifact of this mismatch rather than a genuine improvement in boundary consistency.

Authors: We thank the referee for highlighting this critical point. The manuscript abstract already states that 'The brain masks used for evaluation partially or fully include the subarachnoid space, which may introduce bias into the comparison'. We fully agree that the reported DSC and ASSD values cannot be read as direct quantitative evidence that our model accurately preserves the precise outer surface we targeted during training. Because the silver-standard and external labels include varying amounts of subarachnoid space, the metrics primarily reflect agreement with those particular labels rather than fidelity to our intended boundary (sulcal CSF included, full subarachnoid space and meninges excluded). That said, all comparator methods were evaluated against identical ground-truth masks, so the relative ranking and the notably low variance in our surface-distance metrics remain informative. To address the referee's concern, we will revise the abstract to remove any unqualified claim of 'superiority' in outer-surface preservation and will add a new paragraph in the Discussion section that explicitly describes the boundary mismatch, its implications for metric interpretation, and the clinical rationale for our chosen target definition. We will also note that future consensus ground-truth datasets aligned with this target would enable stronger validation. revision: partial

Circularity Check

0 steps flagged

No circularity: metrics computed on independent held-out and external data

full rationale

The paper trains a modified U-net using silver-standard ground truth and a signed-distance transform loss, then reports DSC and ASSD on held-out test data drawn from the training distribution plus a fully independent external dataset. These evaluation masks are not used in training or parameter fitting, and the reported numbers are standard post-hoc overlap and surface-distance measures with no equations that reduce them to the training inputs by construction. The boundary-definition mismatch noted in the skeptic headline is a validity concern but does not create a self-referential derivation chain.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The approach rests on standard supervised deep-learning assumptions plus the domain choice of silver-standard labels and the novel loss formulation; no new physical entities are postulated.

free parameters (1)

U-net architecture hyperparameters and loss weighting
Standard network depth, filter counts, and any balancing coefficients in the SDT loss are tuned during training on the silver-standard data.

axioms (1)

domain assumption Silver-standard ground truth masks are sufficiently accurate to serve as training targets
Training relies on these masks without independent verification of their boundary accuracy.

pith-pipeline@v0.9.0 · 5617 in / 1261 out tokens · 68412 ms · 2026-05-16T05:32:16.647598+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

12 extracted references · 12 canonical work pages · 1 internal anchor

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Hoopes, A., Mora, J. S., Dalca, A. V., Fischl, B., and Hoffmann, M., “Synthstrip: skull-stripping for any brain image,”Neuroimage260, 119474 (2022)

work page 2022

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Warfield, S. K., Zou, K. H., and Wells, W. M., “Simultaneous truth and performance level estimation (staple): an algorithm for the validation of image segmentation,”IEEE Trans. Med. Imaging23(7), 903– 921 (2004)

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Harms, M. P., Somerville, L. H., Ances, B. M., Andersson, J., Barch, D. M., Bastiani, M., Bookheimer, S. Y., Brown, T. B., Buckner, R. L., Burgess, G. C., et al., “Extending the human connectome project across ages: Imaging protocols for the lifespan development and aging projects,”Neuroimage183, 972–984 (2018)

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Souza, R., Lucena, O., Garrafa, J., Gobbi, D., Saluzzi, M., Appenzeller, S., Rittner, L., Frayne, R., and Lotufo, R., “An open, multi-vendor, multi-field-strength brain mr dataset and analysis of publicly available skull stripping methods agreement,”NeuroImage170, 482–494 (2018)

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Unbiased nonlinear average age-appropriate brain templates from birth to adulthood,

Fonov, V. S., Evans, A. C., McKinstry, R. C., Almli, C. R., and Collins, D. L., “Unbiased nonlinear average age-appropriate brain templates from birth to adulthood,”Neuroimage47, S102 (2009)

work page 2009

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U-net: Convolutional networks for biomedical image segmen- tation,

Ronneberger, O., Fischer, P., and Brox, T., “U-net: Convolutional networks for biomedical image segmen- tation,” in [Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015],Lecture Notes in Computer Science9351, 234–241 (2015)

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V-net: Fully convolutional neural networks for volumetric medical image segmentation,

Milletari, F., Navab, N., and Ahmadi, S.-A., “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in [Proc. 2016 Fourth Int. Conf. on 3D Vision (3DV)], 565–571 (2016)

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Optuna: A Next-generation Hyperparameter Optimization Framework

Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M., “Optuna: A next generation hyperparameter optimization framework.” arXiv preprint arXiv:1907.10902 (July 2019). 7

work page internal anchor Pith review Pith/arXiv arXiv 1907