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REVIEW 4 major objections 4 minor 40 references

3D Skin Segmentation Methods in Medical Imaging: A Comparison

T0 review · 4 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This paper reports that an iterative region-growing skin segmentation method outperforms a CT-trained deep learning model on MRI, while the AI model provides superior automation and bed removal on CT.

desk verdict The survey is fine and the qualitative observations are plausible, but the quantitative comparison lacks registration and uses cross-modality surface distance as an accuracy proxy, so the headline claim is not established. read the letter →

arxiv 2506.11852 v1 pith:LEXPO5MJ submitted 2025-06-13 eess.IV

Martina Paccini , Giuseppe Patanè This is my paper · ORCID
classification eess.IV
keywords 3DskinsegmentationmedicalimageTotalSegmentatorregiongrowingMRICTHausdorffdistancemultimodalimaging
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

Skin segmentation creates the outer body surface used for multimodal image registration, surgical planning, and patient-specific simulation. This paper compares two representative approaches: a graphics-based iterative region-growing algorithm and TotalSegmentator, a deep-learning model trained on CT. The authors claim that when MRI is involved, the graphics-based method is the more reliable choice because the AI model struggles with MR sequences it was not trained on, while the AI method wins on automation and cleanly removes the patient bed in CT. The quantitative support comes from comparing CT and MR skin surfaces of the same subjects and measuring their distances. The practical conclusion is that method choice should follow the imaging modality and whether full automation or cross-modality accuracy matters more.

What carries the argument

The comparison rests on three components. The graphics-based method is a single-seed iterative region-growing algorithm: it floods the volume from a background pixel, marks voxels above an automatically computed skin isovalue as boundary, labels the interior, and applies this per slice with cost linear in the voxel count. For normalized intensities the paper reports that an isovalue of 0.1 works for T2 MRI and CT, while fully automated T2 MRI segmentation uses a gradient image with an isovalue of 0.01. TotalSegmentator is a deep-learning network trained on CT with 104 anatomical structures, including the body and skin; it is fully automatic but tied to its CT training distribution. The quantitative evaluation extracts each skin surface as a mesh with Marching Cubes and compares surfaces using the Hausdorff distance and the mean distance distribution.

What would settle it

Segment both modalities in a dataset with expert-drawn skin contours in co-registered CT and MRI, and compare each method's surface to those contours; if TotalSegmentator is as close or closer to the expert MRI skin than the graphics-based method, the central claim fails. Alternatively, recompute the 52-subject Hausdorff and mean distances after explicit rigid registration and check whether the graphics-based advantage persists.

Watch

Extended reading notes

Core claim

The paper's central claim is that neither approach dominates across all modalities. On CT, TotalSegmentator produces clean skin surfaces, removes the patient bed automatically, and needs no manual tuning; on MRI, it often fails to follow body boundaries because its training data are CT-only. The graphics-based region-growing method segments both CT and MRI without retraining, tracks the MRI skin boundary more closely, and runs in time linear in voxel count on standard hardware, but it leaves the patient bed in CT and yields noisier surfaces. On the 52-subject multimodal dataset, accuracy is defined as agreement between the CT-derived and MR-derived skin surfaces of the same subject, and the paper reports lower Hausdorff and mean distances for the graphics-based method, which it interprets as better cross-modality segmentation.

Load-bearing premise

The quantitative comparison defines segmentation quality as agreement between the CT and MR skin surfaces of the same subject, yet the paper reports no registration or ground truth to establish that the two acquisitions are aligned; if they are not, the measured distances reflect alignment error rather than segmentation error.

Editorial extensions

If this is right

  • Multimodal workflows that must fuse CT with MRI should use the graphics-based method for the MRI skin surface rather than retraining or fine-tuning a CT-only network.
  • CT-only automated pipelines, especially where removing the patient bed matters, can rely on TotalSegmentator with little or no manual intervention.
  • The graphics-based method stays accurate on sub-sampled images and runs without a GPU, making it a candidate for resource-constrained settings.
  • Skin surfaces from either method can support surgical planning tasks such as localizing spinal landmarks for trajectory guidance.

Reading between the lines

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

  • If the MRI gap is caused by training distribution, then fine-tuning TotalSegmentator on MRI or applying unsupervised domain adaptation should shrink or reverse the reported advantage; the paper itself points to domain adaptation as future work.
  • The paper's cross-modality coincidence metric likely mixes segmentation error with CT-MR misregistration and differences in patient positioning, since no registration or ground-truth contours are reported.
  • The complementary failure modes (noise from the graphics method, holes and bed artifacts handled by the AI) suggest a hybrid pipeline could be tested: graphics-based surface extraction plus AI-based bed removal and hole filling.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper compares a graphics-based iterative region-growing skin segmentation method (Paccini et al., 2024) with TotalSegmentator, a deep-learning tool, across CT and MRI acquisitions. The authors report qualitative and quantitative comparisons using the Hausdorff distance and visual inspection, and conclude that TotalSegmentator excels at automation but struggles with MRI because of its CT-based training, while the graphics-based method performs better on MRI but introduces more noise. The quantitative analysis in Section 3.2 compares CT and MR skin segmentations of the same subject without registration and without a manual ground truth, treating surface coincidence as a proxy for accuracy.

Significance. If the comparison were valid, the paper would provide practically useful guidance for choosing between algorithmic and AI-based skin segmentation in multimodal imaging. The paper correctly identifies several real trade-offs, such as TotalSegmentator's automatic bed removal versus the graphics-based method's need for manual intervention, and the interpretability advantages of rule-based methods. However, the central quantitative claim is not established because the evaluation design does not measure segmentation accuracy in a sound way. The paper's positive aspects include a clear description of the graphics-based algorithm and a concrete dataset choice; yet the load-bearing experimental evidence is currently inadequate.

major comments (4)
  1. [Section 3.2, Quantitative analysis] The accuracy metric is invalid for the stated purpose. The authors compare segmentations of CT and MR images of the same subject and state that 'the best result corresponds to two coincident surfaces,' which assumes that the two acquisitions are already aligned. No registration step is reported, and the Bauer dataset is a registration benchmark with ground-truth transformations that are not used. Patient positioning, breathing-phase differences, and field-of-view differences (e.g., arms present only in MR) can produce large CT-MR surface distances even for perfect segmentations. Therefore Fig. 4 and the conclusion that the graphics-based method 'achieves better results' on MRI are not supported.
  2. [Section 3.2, Hausdorff distance definition] The distance measure is defined one-sided as dX1(X2) = max_{x in X1} min_{y in X2} ||x-y||_2, which is a directed distance, not the symmetric Hausdorff distance used in medical image evaluation. The surrounding text also mismatches the formula: it says the minimum distance of each vertex of X2 from vertices of X1 is computed, but the formula takes the maximum over X1. Because the measure is not symmetric, the reported values depend on which surface is labeled X1 and can be misleading. Standard symmetric metrics such as Dice similarity coefficient or symmetric surface distance should be used.
  3. [Section 3.2, parameter tuning] The comparison is not like-for-like. The graphics-based method is tuned by the authors for this dataset: a skin isovalue of 0.1 and a gradient isovalue of 0.01 are described as yielding 'optimal results' in Section 3.2. TotalSegmentator, by contrast, is used off-the-shelf with no MRI-specific adaptation or fine-tuning. The conclusion that the graphics-based method 'performs better for MRIs' is therefore partly forced by the evaluation design, because the authors' method has been manually calibrated to the test data while the competing method has not.
  4. [Table 1 and Figures 1-3] The Hausdorff distances reported in Table 1 and Figures 1-3 are distances between the two methods' surfaces, not distances to any reference standard. A low distance can occur when both methods make the same error (e.g., the head MRI case where TotalSegmentator's output 'seems to be related more to the skull than the actual skin'), so these numbers cannot be interpreted as evidence that one method is more accurate. No manual ground truth or standard accuracy metrics appear anywhere in the paper, so the abstract's claim that one method is 'better' on MRI is not backed by a valid quantitative comparison.
minor comments (4)
  1. [Section 3.2] There is a typo in 'Totasegmentator' in the sentence about Figure 1; it should be 'TotalSegmentator'.
  2. [Section 3.2] The notation for the distance is inconsistent: the text and the equation label the surfaces differently, and the equation appears to use a squared norm (||x-y||_2) without clarifying whether the distance is in millimeters or squared millimeters. The units in Table 1 and Figures 2-4 are stated as mm, so the norm should be explicitly defined.
  3. [Figure 2] Figure 2(a) is said to show an abdominal MRI, but the caption does not describe the color coding or the meaning of the distance distribution in detail; a reader must infer from the text which surface is red and which is green.
  4. [Section 4] The conclusion that the graphics-based approach is 'versatile and adaptable' is stated strongly, but the only direct evidence is the flawed quantitative analysis and a few qualitative examples. A more measured conclusion would acknowledge the limitations of the comparison.

Circularity Check

1 steps flagged · score 6.0 of 10

The quantitative evidence for 'graphics-based performs better on MRI' equates accuracy with CT-MR surface coincidence, so the conclusion is partly forced by the evaluation metric rather than by ground-truth comparison.

  1. self definitional [Section 3.2, Quantitative analysis (Fig. 4)]
    "To assess the accuracy of the segmentation methods, we compared the segmentations produced for the same subject across the two imaging modalities, where the best result corresponds to two coincident surfaces."

    The metric defines accuracy as agreement between the CT and MR skin surfaces of the same subject. The graphics-based method is a modality-agnostic threshold/region-growing algorithm that extracts the outer body boundary using the same isovalue after intensity normalisation; therefore its CT and MR surfaces coincide whenever the body outline is present in both scans. TotalSegmentator, trained on CT, produces a different MRI surface and is penalised by the same metric. The conclusion that the graphics-based method 'achieves better results' on MRI (Fig. 4) is thus a restatement of the metric's definition (accuracy = cross-modality invariance), not evidence of agreement with the true skin surface.

full rationale

The paper's central comparative claim is that the graphics-based method performs better for MRIs while TotalSegmentator struggles due to CT training. The visual comparison (Fig. 2a) provides some independent support for this. However, the quantitative analysis that is invoked to substantiate the claim defines accuracy as the coincidence of CT and MR segmentations of the same subject. Since the graphics-based method is designed to be modality-agnostic (same threshold-based boundary extraction regardless of modality), high cross-modal coincidence is built into the method; TotalSegmentator's modality-specific training ensures lower coincidence. The quantitative conclusion therefore reduces to the definition of the metric. This is a partial circularity: the metric's definition and the method's design jointly force the reported ranking. The self-citation to Paccini et al. (2024) for the method's 'generality' is not itself load-bearing for the experiments, and the paper contains independent visual evidence and computational comparisons, so the score is moderate rather than extreme.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The central claim is an empirical comparison rather than a derivation, so the main free parameters are the hand-tuned isovalues of the graphics method. The load-bearing axioms are the questionable cross-modality accuracy proxy and the one-sided distance metric. No new entities are introduced.

free parameters (2)
  • skin isovalue = 0.1
    Used for T2 MR and CT after intensity normalization; chosen by the authors to achieve 'precise segmentation' (Section 3.2).
  • gradient isovalue for automated MRI = 0.01
    Chosen for fully automated T2 MRI segmentation after computing the gradient image (Section 3.2).
assumptions (4)
  • ad hoc to paper Surface agreement between CT and MR segmentations of the same subject is a valid measure of segmentation accuracy.
    This assumption underlies the quantitative analysis in Section 3.2 ('the best result corresponds to two coincident surfaces'). No registration or ground truth is provided.
  • domain assumption The public dataset by Bauer et al. (Zöllner, 2022; Bauer et al., 2021) is a representative benchmark for skin segmentation.
    The 52-subject evaluation relies on this dataset and its inhale/exhale CT and MR scans, but no argument is given for representativeness.
  • ad hoc to paper The one-sided maximum-minimum distance defined in the paper is an acceptable surface difference measure.
    Section 3.2 defines dX1(X2) as a one-sided distance rather than the symmetric Hausdorff distance; this non-standard choice affects all reported distances.
  • standard math Marching Cubes (Lorensen and Cline, 1998) produces a faithful mesh of the segmented voxel surface.
    Used to extract the meshes that are compared, without validation of mesh fidelity.

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Pith. "Pith review of 3D Skin Segmentation Methods in Medical Imaging: A Comparison." pith.science (2026). https://pith.science/paper/LEXPO5MJ

@misc{pith2026250611852,
  author       = {Pith},
  title        = {Pith review of: 3D Skin Segmentation Methods in Medical Imaging: A Comparison},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LEXPO5MJ}},
  note         = {Machine review of arXiv:2506.11852}
}
read the original abstract

Automatic segmentation of anatomical structures is critical in medical image analysis, aiding diagnostics and treatment planning. Skin segmentation plays a key role in registering and visualising multimodal imaging data. 3D skin segmentation enables applications in personalised medicine, surgical planning, and remote monitoring, offering realistic patient models for treatment simulation, procedural visualisation, and continuous condition tracking. This paper analyses and compares algorithmic and AI-driven skin segmentation approaches, emphasising key factors to consider when selecting a strategy based on data availability and application requirements. We evaluate an iterative region-growing algorithm and the TotalSegmentator, a deep learning-based approach, across different imaging modalities and anatomical regions. Our tests show that AI segmentation excels in automation but struggles with MRI due to its CT-based training, while the graphics-based method performs better for MRIs but introduces more noise. AI-driven segmentation also automates patient bed removal in CT, whereas the graphics-based method requires manual intervention.

Figures

Figures reproduced from arXiv: 2506.11852 by the authors.

Figure 1
Figure 1. Skin surfaces segmented from a high (a-b, left) and low resolution (a-b, right) image. According to the [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Skin segmentation from an abdominal MRI based on (red) the TotalSegmentator and (green) the graphics [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Comparison of the segmentations on a subsampled image. (a) Super-imposition of the two surfaces, (b) [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Comparison of the mean distance (a) and Hausdorff distance (b) distribution on the whole dataset for the [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Visualization support for surgical planning: the localisation of anatomical landmarks on the skin surface can [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Coregistration error distance distribution on the surface (a). Corresponding error distribution inside the single [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.