A Unified Framework for Foreground and Anonymization Area Segmentation in CT and MRI Data

Constantin Ulrich; Jonathan Suprijadi; Klaus Maier-Hein; Michal Nohel; Tassilo Wald

REVIEW 2 major objections 4 minor 19 references

A Unified Framework for Foreground and Anonymization Area Segmentation in CT and MRI Data

T0 review · 2 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This paper claims that two nnU-Net segmenters—one for anatomical foreground, one for anonymized facial regions—can be trained once on mixed CT and MRI data and then preprocess unannotated 3D scans for self-supervised learning, with mean…

desk verdict Useful, well-packaged preprocessing toolkit for SSL, but the CT foreground generalization claim is weaker than it looks because the external test shares the labeling protocol. read the letter →

arxiv 2501.04361 v1 pith:MJY2544Y submitted 2025-01-08 eess.IV cs.CV

Michal Nohel , Constantin Ulrich , Jonathan Suprijadi , Tassilo Wald , Klaus Maier-Hein This is my paper · ORCID

classification eess.IVcs.CV

keywords foregroundsegmentationanonymizationself-supervisedlearning3DmedicalimagingCTMRIdefacennU-Net

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper aims to remove two obstacles to self-supervised learning on 3D medical images: much of a scan is empty air, wasting computation, and anonymized facial regions inject distorted signals into reconstruction-based SSL losses. It builds an open-source toolkit with two segmentation networks, one that marks all anatomical foreground voxels in CT and MRI and one that marks defaced, refaced, or blurred regions. On internal test splits the foreground network reaches a mean Dice of 99.56, and on external CT data 98.57; the anonymization network stays at or above 98.5 mean Dice across deface, reface, reface-plus, and an external blurred-face set. The authors conclude the models are accurate and dependable enough to serve as preprocessing tools for 3D medical SSL.

What carries the argument

The machinery is a pair of nnU-Nets, a self-configuring deep-learning segmentation architecture, one trained to label anatomical foreground and one trained to label anonymized regions. Both use a 192 by 192 by 192 patch size and z-score normalization so that a single set of weights covers CT and MRI. The foreground labels were produced by taking an existing automatic body-region segmentation tool's output, manually refining it, and then using an initial model to speed up labeling the remaining MRI volumes; the anonymization labels come from applying three common defacing algorithms to non-anonymized scans. The network outputs are intended as masks: the foreground map guides patch sampling, and the anonymization map tells a reconstruction loss which voxels to ignore.

What would settle it

Have independent experts manually trace foreground and anonymized regions on a sample of CT and MRI volumes that were never processed by the paper's labeling pipeline, then compare network predictions to those manual masks with Dice and HD95; if the scores fall far below 99.56 and 98.57 for foreground or below 98.5 for deface, the generalization claim is refuted.

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Extended reading notes

Core claim

The central claim is that a single nnU-Net, trained on a mixed CT and MRI dataset with a 192 by 192 by 192 patch size and z-score normalization, can segment the anatomical foreground accurately enough for both modalities, and a second nnU-Net can localize anonymized facial regions across three anonymization schemes plus real-world blurred faces. On the test portion of the training data, the foreground model reaches a mean Dice of 99.56; on an external CT dataset it reaches 98.57. For anonymization, the reported mean Dice values are 99.05 for deface, 99.50 for reface, 99.01 for reface plus, 92.90 on the external OpenNeuro-style collection, and 98.67 on the external blurred-face subset. The authors argue these results make it possible to sample only informative patches and to mask altered regions out of SSL reconstruction losses without needing dataset-specific adapters.

Load-bearing premise

The claim depends on the ground-truth masks being correct, especially the CT foreground labels that come mostly from an automatic segmentation tool with manual refinements; if that tool has systematic biases, Dice scores measured against its masks overstate real accuracy on independently labeled scans.

Editorial extensions

If this is right

Foreground masks let an SSL trainer sample patches only from anatomical content, so training runs avoid the large air-filled parts of 3D scans.
Anonymization masks can be fed into reconstruction-based SSL losses, so defaced, refaced, or blurred voxels do not produce erroneous supervision signals.
Because one foreground model handles both CT and MRI with the same weights and z-score normalization, a single preprocessing step replaces modality-specific pipelines.
The deface model's coverage of all three common anonymization schemes and an external blurred-face dataset means it should apply to the main ways public medical data are currently altered.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The external CT evaluation is weaker than it looks: the external CT test set was labeled with the same segmentation pipeline that produced the CT training masks, so the reported external Dice may reflect labeling-protocol overlap rather than generalization to independently drawn ground truth.
The two masks compose naturally: an SSL loss mask of 'foreground AND not anonymized' would combine both networks and is the direct practical use the paper leaves implicit.
The foreground network's stated failure mode on scans with large empty fields and constant-value reconstruction boundaries suggests a cheap pre-filter that flags such slabs would remove most remaining errors; this extension is not tested in the paper.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague,

This is a toolkit paper, and judged as one it is mostly fine. The authors train nnU-Net models for two tasks the SSL community actually needs: foreground segmentation to sample patches efficiently in 3D, and anonymized-region segmentation to mask out defaced areas from reconstruction losses. They report Dice above 98.5 across the board, ship code and weights, and document the label-generation pipeline. That is real value. The MRI foreground model is genuinely new, since TotalSegmentator does not work on MRI, and the paper shows a sensible way to bootstrap labels with thresholding, manual correction, and iterative refinement. The deface model covers three AFNI anonymization modes and is validated externally on OpenNeuro, which is a reasonable setup.

The soft spot is the CT foreground evaluation, and it is load-bearing. Every CT foreground mask in the paper, training and the MAAL "external" test, comes from the same pipeline: TotalSegmentator body mode plus manual refinement. So the 98.57 mean Dice on MAAL does not demonstrate generalization to an independent labeling protocol; it demonstrates that the model reproduces TotalSegmentator's conventions about where the body ends, where table edges lie, and how field-of-view boundaries are handled. The paper even shows failure cases where those conventions break down. The deface segmentation is on firmer ground because OASIS3 masks are generated by AFNI and OpenNeuro provides independently derived masks, though the blurring test set involves only 14 manual annotations.

A second gap: the abstract and intro promise SSL benefits, reduced training time and avoiding erroneous supervision, but no SSL experiment is run. That is an omission, not a deal-breaker, but it leaves the motivating claim unverified.

I agree with the conditional verdict. The central segmentation results are likely reproducible and useful, but the overstatement about external robustness should be fixed. The right remedy is an independent label audit on a small CT set, say 20-30 scans manually labeled by a different rater, and ideally a simple SSL proof-of-concept showing time savings or a downstream gain. That would turn a plausible toolkit into a solid one.

I would bring this to reading group only if someone is about to use the toolkit. It deserves peer review, as a competent, well-documented engineering contribution with public assets, but it needs honest revision before acceptance. Recommend a major revision addressing the label circularity and either adding the SSL validation or toning down the claims.

Referee Report

2 major / 4 minor

Summary. The paper presents an open-source toolkit consisting of two nnU-Net models for medical imaging preprocessing: a foreground segmentation model that delineates anatomical foreground in CT and MRI scans, and an anonymization-area segmentation model that identifies defaced, refaced, and blurred regions. The foreground model is trained on a multi-dataset collection with labels produced by TotalSegmentator 'body mode' plus manual refinement, and the deface model is trained on OASIS3 images artificially anonymized with AFNI and tested on OpenNeuro data. Results report high Dice coefficients (mean 99.56 on the in-distribution test split, 98.57 on the external CT/MRI set; mean 92.90-99.50 across deface types) and HD95 values. The authors argue these models enable efficient patch sampling and prevent erroneous supervision in reconstruction-based self-supervised learning (SSL) for 3D medical imaging.

Significance. If the robustness claims hold, the toolkit could be a practical resource for the medical imaging SSL community, particularly because the code and weights are made publicly available and the deface evaluation includes a reasonably independent OpenNeuro test set with externally provided masks. The paper also merits credit for honest reporting of failure cases and for using standard, reproducible nnU-Net methodology. However, the central generalization claim for CT foreground is weakened by label-protocol circularity, and the claimed SSL benefits are not demonstrated by any experiment. The significance is therefore conditional: the contribution is a potentially useful engineering resource, but the scientific validation as presented is incomplete.

major comments (2)

[§2 Materials and methods, Anatomical foreground segmentation; §3 Results] The external CT foreground evaluation is compromised by label-protocol overlap. The text states that 'a multi-step approach was applied to obtain anatomical foreground masks for both CT and MRI images' immediately after listing all datasets, including MAAL, and no independent label source is described for MAAL. Since the same TotalSegmentator 'body mode' plus manual-refinement pipeline produced both the training labels and the MAAL test labels, the mean Dice of 98.57 on the 'external' test set (Fig. 1) largely measures the model's ability to replicate a labeling protocol rather than its generalization to an independent foreground definition. The same issue applies to the reserved TotalSegmentator MRI test set, whose labels were generated by the same semi-automatic workflow. The boundary-sensitive failure cases in Fig. 3b and 3c make this concern concrete. Please add an independently labeled CT foreground test set (e.g., manual body-region delineations from another source), report per-dataset results, or explicitly re-frame the MAAL and TotalSegmentator MRI results as protocol-replication checks rather than evidence of external generalization.
[§3 Results; §4 Discussion; Abstract] The paper's motivating claims about SSL utility are not tested. The abstract and discussion state that the foreground model 'reduces training time' by enabling selective patch sampling and that the deface model prevents 'erroneous supervision in reconstruction-based SSL methods', but no SSL method is trained or evaluated, and no wall-clock training-time or throughput measurements are reported. The reported Dice and HD95 scores, while impressive, do not by themselves establish that the toolkit improves SSL preprocessing in practice. Please add at least one experiment that integrates both models into a representative SSL pipeline, comparing training time and downstream task performance with and without the toolkit, or substantially soften the claims to describe the models as segmentation tools whose SSL benefit remains to be demonstrated.

minor comments (4)

[§2 Deface area segmentation; Table 2] The 'OpenNeuro - external' row in Table 2 is described in the text as 14 manually annotated images, but the reported standard deviation of 0.05 percentage points for both Dice and HD95 is implausibly small for 14 images and suggests either a misreport or a much smaller sample; please clarify the exact number of images and the annotation protocol.
[Fig. 1 and Fig. 2; §3 Results] The boxplots aggregate results across datasets and anonymization types; please report the number of cases per group, per-dataset medians, and the whisker/IQR definitions so readers can assess variability and outliers.
[§2 Deface area segmentation] The sentence 'with some datasets providing original deface masks originating from theDefaced method' appears to contain a typo ('theDefaced') and should also specify which OpenNeuro sub-datasets and which anonymization methods are included.
[Abstract and elsewhere] The abstract contains the grammatical error 'The weights and code is available'; consider adding a dedicated Code/Data Availability section and using 'are available'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the paper reports empirical benchmark measurements and its self-citations are methodological, not load-bearing.

full rationale

The paper's contributions are empirical toolkits: two nnU-Net segmentation models trained on explicitly described ground-truth masks and evaluated with Dice/HD95. No equation-level derivation is offered, so there is no self-definitional reduction. The foreground CT labels do all originate from the same TotalSegmentator 'body mode' plus manual refinement pipeline, including the MAAL 'external' test set; this limits the external-generalization claim to image origin rather than label-protocol independence, but it is not circular in the sense of a fitted parameter renamed as a prediction, because MAAL is a held-out image set and the model is not constructed to reproduce its labels by definition. The deface component is evaluated against OpenNeuro masks produced by a different tool (Defaced) and against manual annotations, providing independent evidence. Self-citations to nnU-Net, TotalSegmentator, and related group tools are methodological and do not carry a uniqueness argument that forbids alternatives. Overall the reported numbers are benchmark measurements, not implications from assumed conclusions, so no circular step is exhibited.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The central claim relies on two label-generation pipelines (TotalSegmentator for CT foreground and AFNI for deface masks). These are external tools and are not purely circular, but the foreground evaluation is partially self-referential because the same tool provides training and external test labels. The model is not a derivation, so no mathematical circularity applies.

assumptions (4)

domain assumption TotalSegmentator-derived masks with manual refinement are accurate foreground ground truth for CT
Used to generate training labels and, presumably, test labels; systematic errors would bias Dice scores.
domain assumption AFNI-generated deface masks are exact ground truth
The network learns to predict AFNI's defacing; if AFNI's masks are imperfect, the network inherits errors.
domain assumption The iterative MRI labeling pipeline (network prediction plus manual refinement) produces labels equivalent to fully manual annotation
Minor refinements may not correct all network errors, especially in ambiguous backgrounds.
domain assumption nnU-Net with z-score normalization can handle both CT and MRI in one network
The authors rely on this design choice; the evidence is the high reported Dice, but it is an assumption about transferability.

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Cite this review

Pith. "Pith review of A Unified Framework for Foreground and Anonymization Area Segmentation in CT and MRI Data." pith.science (2026). https://pith.science/paper/MJY2544Y

@misc{pith2026250104361,
  author       = {Pith},
  title        = {Pith review of: A Unified Framework for Foreground and Anonymization Area Segmentation in CT and MRI Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MJY2544Y}},
  note         = {Machine review of arXiv:2501.04361}
}

read the original abstract

This study presents an open-source toolkit to address critical challenges in preprocessing data for self-supervised learning (SSL) for 3D medical imaging, focusing on data privacy and computational efficiency. The toolkit comprises two main components: a segmentation network that delineates foreground regions to optimize data sampling and thus reduce training time, and a segmentation network that identifies anonymized regions, preventing erroneous supervision in reconstruction-based SSL methods. Experimental results demonstrate high robustness, with mean Dice scores exceeding 98.5 across all anonymization methods and surpassing 99.5 for foreground segmentation tasks, highlighting the efficacy of the toolkit in supporting SSL applications in 3D medical imaging for both CT and MRI images. The weights and code is available at https://github.com/MIC-DKFZ/Foreground-and-Anonymization-Area-Segmentation.

Figures

Figures reproduced from arXiv: 2501.04361 by the authors.

**Figure 1.** Boxplot results illustrating the Dice Similarity Coefficient and HD95 for foreground segmentation, shown for both the test portion of the training dataset and the external test dataset [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

**Figure 2.** [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Example predictions for foreground (FG) segmentation and anonymization segmentation. While in the majority of cases, the FG segmentation is almost perfectly solved (a), we observed a rare failure case for volumes with an artificial boundary between actual air and a region with constant values introduced during image reconstruction(b). Further we observed problems in images without a sharp contrast between the bac… view at source ↗

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Reference graph

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 pp

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