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REVIEW 4 major objections 6 minor 30 references

Automated surgical planning with nnU-Net: delineation of the anatomy in hepatobiliary phase MRI

T0 review · 4 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read A single automated MRI segmentation model can produce clinically usable 3D liver models in about 15 minutes per patient.

desk verdict Solid single-center clinical integration study for HBP-MRI liver segmentation, with real workflow gains, but the quantitative evaluation is thinner than the standard-of-care conclusion suggests. read the letter →

arxiv 2508.14133 v1 pith:G476NGVM submitted 2025-08-19 eess.IV cs.AIcs.CV

Karin A. Olthof , Matteo Fusagli , Bianca Güttner , Tiziano Natali , Bram Westerink , Stefanie Speidel , Theo J.M. Ruers , Koert F.D. Kuhlmann
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Andrey Zhylka
This is my paper · ORCID
classification eess.IVcs.AIcs.CV
keywords liverMRIautomatedsegmentationpatient-specific3DmodelssurgicalplanningsurgerynnU-NethepatobiliaryphaseclDice
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

This paper sets out to show that one deep-learning segmentation network, trained on hepatobiliary-phase MRI scans, can produce the full set of anatomical outlines a liver surgeon needs before an operation: the liver itself, tumors, portal and hepatic veins, and the biliary tree. The authors trained an nnU-Net on 72 manually segmented patients with a topology-preserving loss aimed at keeping thin vessels connected, then tested it on 18 unseen patients. In prospective clinical use the automated segmentations needed only minor corrections for the main structures, reducing the 3D-modeling step from several hours to about 15 minutes per patient and identifying three sub-centimeter tumors that radiologists had initially missed. If these results generalize, 3D planning could become a routine component of liver-surgery workup rather than a specialized manual service.

What carries the argument

The mechanism that carries the argument is an nnU-Net v1 segmentation network trained with a composite loss: clDice plus bootstrapped cross-entropy. clDice is a topology-preserving loss that skeletonizes the ground-truth and predicted vessel trees and penalizes disconnected or missing tubular structures; it is what keeps thin portal and hepatic vein branches and small bile ducts attached to the main tree. Bootstrapped cross-entropy selects only the hardest voxels for the loss, after a 400-epoch warm-up with ordinary cross-entropy, with the top-K fraction growing from 15% to 50% over the final 100 epochs. The input is the 20-minute hepatobiliary phase of a Gd-EOB-DTPA-enhanced 3T MRI, a sequence chosen because it shows both vascular anatomy and bile excretion. Manual segmentations made by two experienced technical physicians and confirmed by a hepatobiliary surgeon provide the reference standard for both training and evaluation.

What would settle it

Segment the same 18 test scans with two or more independent expert annotators and compute their pairwise overlap scores for portal vein, hepatic vein, biliary tree, and tumors; if expert-to-expert agreement is no better than the model's scores on those structures, the model is operating at the limit of the reference standard rather than demonstrating true anatomical accuracy.

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Extended reading notes

Core claim

The central claim is that the hepatobiliary phase of gadoxetic acid-enhanced MRI carries enough contrast information for a self-configuring nnU-Net to delineate all five structures that matter for liver surgery planning, and that the resulting segmentations are clinically usable with only light manual touch-up. On the 18-patient test set, mean Dice similarity coefficients (0-to-1 overlap scores) were 0.97 for liver parenchyma, 0.80 for the hepatic vein, 0.79 for the biliary tree, 0.77 for tumors, and 0.74 for the portal vein; the average tumor detection rate was 76.6%, with a median of one false positive per patient. In the 10-patient assessment dataset collected after integration into clinical practice, the parenchyma reached 1.00, portal vein 0.98, hepatic vein 0.95, and tumor 0.80, with the largest manual corrections needed for tumors in patients with prior liver interventions, high tumor burden, or low scan quality. The workflow time for producing a 3D model fell from several hours to roughly 15 minutes per patient, and the network identified three sub-centimeter malignant lesions that radiologists had not initially reported.

Load-bearing premise

The load-bearing premise is that the manual outlines used for training and evaluation are correct for every structure; if those outlines are noisy, especially for thin vessels and small tumors, the reported overlap scores overstate how precisely the model matches true anatomy.

Editorial extensions

If this is right

  • If these results generalize, 3D liver models can be produced for every patient scheduled for liver surgery, not only those treated at centers that can afford hours of manual segmentation.
  • Preserving vessel-tree topology means the segmentations can serve as a map for image-guided procedures, where vessel bifurcations are used as registration landmarks.
  • Cutting the segmentation step from several hours to about 15 minutes makes 3D planning compatible with normal clinical scheduling on a per-patient basis.
  • Automated tumor outlining adds a safety check: in this cohort it surfaced three sub-centimeter lesions initially missed by the radiologist.
  • The network handles common anatomical variations such as an absent gallbladder after cholecystectomy and regenerated anatomy after prior resection, so it does not fail at the edges of a typical liver-surgery population.

Reading between the lines

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

  • A testable extension is external validation: running the trained network on multi-center MRI data with different scanners and protocols would show whether the reported vessel accuracy holds across imaging setups or is tied to one protocol.
  • Since inter-observer variability of the manual reference was not reported, computing pairwise expert-vs-expert Dice on the same test scans would reveal how much of the model's apparent error is actually reference noise, especially for thin vessels and small tumors.
  • The three incidentally detected sub-centimeter tumors suggest a second-reader role for the model, but establishing that requires a blinded prospective reader study comparing radiologists with and without the model's output.
  • A natural next step is adding diffusion-weighted imaging or arterial-phase input to raise tumor detection beyond the current 76.6%; the trade-off in false positives is directly testable with the same evaluation protocol.
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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 / 6 minor

Summary. This manuscript reports the development and clinical integration of an nnU-Net-based automated segmentation method for hepatic anatomy—liver parenchyma, tumors, portal vein, hepatic vein, and biliary tree—from the hepatobiliary phase of gadoxetic acid-enhanced MRI. Manual segmentations from 90 patients were used to train (n=72) and test (n=18) the model, with an additional assessment cohort of 10 patients where model outputs were manually refined for clinical use. The authors report test-set Dice similarity coefficients of 0.97 for parenchyma, 0.80 for hepatic vein, 0.79 for biliary tree, 0.77 for tumors, and 0.74 for portal vein, together with a mean tumor detection rate of 76.6% and a median of one false positive per patient. In the assessment set, high DSC values are reported for parenchyma, portal vein, and hepatic vein after manual refinement, and the authors state that segmentation time decreased from several hours to about 15 minutes per patient. The paper concludes that the method enables accurate automated delineation and supports broader adoption of 3D planning in liver surgery.

Significance. If the reported performance holds under independent verification, the work has clear translational value: it addresses a real clinical bottleneck in liver surgery planning, uses a clinically relevant MRI phase, and includes a prospective assessment of workflow impact. The comparison with prior MRI-based liver segmentation studies (Zbinden et al., Ivashchenko et al., Oh et al.) is useful positioning. The strengths include a held-out test set, a consecutively collected single-center cohort, clinical integration with manual refinement, and reporting of tumor detection rate and false positives. However, the central quantitative claims rest on three points that are not currently established: the reliability of the manual ground truth, the contribution of the customized loss function, and the interpretation of the assessment-set DSC as evidence of accuracy. These are load-bearing because the weakest structures (portal vein, biliary tree, tumors) are exactly those where annotation variability and loss-function choices are most consequential.

major comments (4)
  1. [Methods, Manual segmentations; Results, Quantitative evaluation] The manuscript uses manual segmentations by two technical physicians, confirmed by a hepatobiliary surgeon, as ground truth for all five structures, but reports no inter-observer variability, no repeat-segmentation study, and no independent second contour for any structure. This is load-bearing because the structures with the lowest DSCs—portal vein (0.74±0.06), biliary tree (0.79±0.07), hepatic vein (0.80±0.04), and tumors (0.77±0.17)—are precisely the structures where boundary definition and lesion identification are most subjective. Without a human-human DSC baseline, the reader cannot separate model error from annotation noise, and the conclusion that the model provides 'accurate delineation' is not supported. Please provide an inter-observer variability measurement (e.g., DSC between independent manual segmentations) on a representative subset, or alternatively report the uncertainty this introduces into all quoted DSC values.
  2. [Methods, Automated segmentation model] The methodological claim that the combination of clDice and bootstrapped cross-entropy improves thin-structure delineation and topology preservation is not tested. The paper reports no ablation against a standard nnU-Net baseline (e.g., default cross-entropy plus Dice loss) on the same training and test splits. Since nnU-Net v1 with default settings is a strong baseline, the observed DSC values could be attributable to the architecture and data rather than to the customized loss. Moreover, the bootstrapped cross-entropy schedule (K growing from 15% to 50% over the final 100 epochs after a 400-epoch warm-up) introduces several free hyperparameters without sensitivity analysis. Please add an ablation study comparing (i) default nnU-Net, (ii) default nnU-Net plus clDice, and (iii) the full proposed loss combination, reporting DSC and topology metrics for at least the vascular and biliary structures.
  3. [Results, Performance of the network in prospective use; Discussion] The assessment-set evaluation compares the automated segmentations to the manually refined versions of those same automated segmentations. A high DSC in this comparison primarily measures the amount of editing performed, not the anatomical accuracy of the model, and the manual refinement process is likely biased toward minimal changes rather than independent re-delineation. The statement that 'minor adjustments were required for clinical use' is therefore not equivalent to anatomical accuracy. Please either compare assessment-set outputs to an independent manual segmentation of the assessment scans, or reframe this analysis explicitly as a measure of editing effort, supported by quantitative edit-distance or time metrics rather than DSC.
  4. [Discussion, Limitations; Results, Prospective tumor detection] The paper claims that 'the model detected three additional tumors initially missed by radiologists,' but this is an anecdotal observation from prospective clinical use without a systematic reference standard or an evaluation protocol for detection sensitivity in the assessment cohort. Given that the test-set tumor detection rate is only 76.6% with a median of one false positive per patient, this claim should be contextualized as a case observation, not a quantitative result. The external-validation limitation is explicitly acknowledged in the Discussion, and I agree it is important; however, given the conclusion advocating standard-of-care use, the absence of external or multi-center validation should also be reflected in the abstract and conclusion as a qualification of the generalizability claim.
minor comments (6)
  1. [Results, Quantitative evaluation; Figure 2] The text reports a combined DSC of 0.84±0.06 for 'gallbladder (central biliary tree) and bile ducts (peripheral biliary tree),' while the overall biliary tree DSC is 0.79±0.07. Please clarify whether these are separate structures or pooled in the figure, and define how cholecystectomy cases are handled in the calculation of the combined value.
  2. [Table 1] The pixel-spacing row reads '-1.5 1' and the footnote explains that negative pixel spacing indicates partially overlapping slices, but this formatting is confusing. Please present the acquisition and reconstruction voxel sizes more clearly, and specify which spacing value is used by nnU-Net for resampling.
  3. [Statistical analysis] The statistical analysis section states that DSC scores were compared using an unpaired Mann-Whitney test, but it does not state which groups were compared. Please specify the comparisons (e.g., central vs. peripheral vessels, training vs. test demographics) and correct the Python/SciPy version and the SPSS version if needed.
  4. [Introduction and Discussion] Reference 29 (Strahler, 1957) is listed in the references but does not appear to be cited in the main text. If it is used for the notion of vessel topology or branching order, please cite it explicitly where that concept is introduced.
  5. [Throughout] The framework is referred to inconsistently as 'nnUNet' and 'nnU-Net'; please use a single consistent spelling, preferably 'nnU-Net' as in the reference to Isensee et al.
  6. [Discussion, comparison with prior work] The comparison with Oh et al. reports higher DSC values for the proposed method across all structures, but no statistical comparison or matching of annotation protocols is provided. Please state whether the comparison is descriptive only, and note any differences in tumor inclusion criteria, vessel definition, or evaluation methodology that could affect the comparison.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: test-set evaluation is held out, the loss design is not fitted to the test set, and the assessment-set DSCs are transparently framed as editing-effort measurements.

full rationale

The paper's central claim is an empirical performance evaluation rather than a derivation, so the main circularity patterns do not apply. Test-set DSCs (Figure 2) are computed on an 18-patient held-out split against manual segmentations that were not used to fit the network; the clDice + bootstrapped cross-entropy schedule was fixed before evaluation and was not tuned on the test set. The clDice citation (ref. 25) includes a co-author of this paper, but it is used as a published, externally implemented loss function, and no uniqueness or forced-choice argument rests on it. The assessment dataset compares the network output with a manual refinement of that same output; the manuscript explicitly labels this as quantifying required manual adjustments/editing effort ("quantify required adjustments using DSC"), not as an independent anatomical validation, so it does not constitute a prediction smuggled in as accuracy. Absence of inter-observer variability and of external validation are correctness/robustness limitations rather than circularity. No step in the paper reduces by construction to its own inputs.

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

The central claim rests on the validity of manual ground truth, the representativeness of a single-center cohort, and the assumed benefit of the chosen losses. No new entities are introduced. Hyperparameters of the loss schedule are hand-chosen and not ablated.

free parameters (4)
  • Bootstrapped cross-entropy K schedule = K grows linearly from 15% to 50% over the final 100 of 500 epochs
    Hand-chosen to focus training on hard voxels; no sensitivity analysis or ablation is provided to show the benefit.
  • Warm-up duration for bootstrapped cross-entropy = 400 epochs with K=100%
    Hand-chosen; affects training dynamics and final performance, but the paper does not explore alternatives.
  • Total training length = 500 epochs
    Fixed schedule without early stopping or convergence analysis; no epoch selection criterion is described.
  • Loss combination weights = not specified
    Combination of clDice and bootstrapped cross-entropy is described without stating how the two losses are weighted, leaving an undisclosed hyperparameter.
assumptions (4)
  • domain assumption Manual segmentations are accurate ground truth for all structures.
    Evaluation uses physician-produced outlines as reference; without inter-observer variability, label noise is unquantified. (Methods, Manual segmentations)
  • domain assumption The 90-patient single-center cohort represents the clinical population for standard-of-care deployment.
    All scans are from one institution and one scanner; no external or multi-center validation is performed, as acknowledged in the Discussion.
  • domain assumption clDice and bootstrapped cross-entropy improve topologically accurate vessel segmentation on this task.
    The paper relies on prior published evidence for these losses; no internal ablation isolates their contribution to the reported DSCs. (Methods, Automated segmentation model)
  • domain assumption Spatial resolution of 2.0x2.0x3.0 mm is adequate for delineating the target structures.
    The study assumes the available clinical resolution captures the thin vessels and bile ducts that the model is asked to segment. (Table 1)

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Cite this review

Pith. "Pith review of Automated surgical planning with nnU-Net: delineation of the anatomy in hepatobiliary phase MRI." pith.science (2026). https://pith.science/paper/G476NGVM

@misc{pith2026250814133,
  author       = {Pith},
  title        = {Pith review of: Automated surgical planning with nnU-Net: delineation of the anatomy in hepatobiliary phase MRI},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/G476NGVM}},
  note         = {Machine review of arXiv:2508.14133}
}
read the original abstract

Background: The aim of this study was to develop and evaluate a deep learning-based automated segmentation method for hepatic anatomy (i.e., parenchyma, tumors, portal vein, hepatic vein and biliary tree) from the hepatobiliary phase of gadoxetic acid-enhanced MRI. This method should ease the clinical workflow of preoperative planning. Methods: Manual segmentation was performed on hepatobiliary phase MRI scans from 90 consecutive patients who underwent liver surgery between January 2020 and October 2023. A deep learning network (nnU-Net v1) was trained on 72 patients with an extra focus on thin structures and topography preservation. Performance was evaluated on an 18-patient test set by comparing automated and manual segmentations using Dice similarity coefficient (DSC). Following clinical integration, 10 segmentations (assessment dataset) were generated using the network and manually refined for clinical use to quantify required adjustments using DSC. Results: In the test set, DSCs were 0.97+/-0.01 for liver parenchyma, 0.80+/-0.04 for hepatic vein, 0.79+/-0.07 for biliary tree, 0.77+/-0.17 for tumors, and 0.74+/-0.06 for portal vein. Average tumor detection rate was 76.6+/-24.1%, with a median of one false-positive per patient. The assessment dataset showed minor adjustments were required for clinical use of the 3D models, with high DSCs for parenchyma (1.00+/-0.00), portal vein (0.98+/-0.01) and hepatic vein (0.95+/-0.07). Tumor segmentation exhibited greater variability (DSC 0.80+/-0.27). During prospective clinical use, the model detected three additional tumors initially missed by radiologists. Conclusions: The proposed nnU-Net-based segmentation method enables accurate and automated delineation of hepatic anatomy. This enables 3D planning to be applied efficiently as a standard-of-care for every patient undergoing liver surgery.

Figures

Figures reproduced from arXiv: 2508.14133 by the authors.

Figure 1
Figure 1. Flow diagram depicting the study cohort selection from 169 consecutively performed image-guided liver procedures. After exclusion of CT and non-hepatobiliary phase MR scans, 90 hepatobiliary phase MR images were manually segmented. MRI scans and corresponding segmentations were divided into a training dataset (n = 72) and test dataset (n = 18) to train a nn￾Unet. Following clinical integration, automated segmentatio… view at source ↗
Figure 2
Figure 2. Dice similarity coefficients of the test dataset compared with manual segmentations. The violin plots illustrate the median, range, and distribution of the Dice scores. Central vascular segmentations included main trunks and primary branches directly arising from them, and peripheral regions included all smaller branches. Biliary tree segmentation included the gallbladder (central) and the bile ducts (peripheral). P… view at source ↗
Figure 3
Figure 3. Dice similarity coefficient achieved by comparing the segmentations produced for the assessment dataset and the final manually adjusted segmentations for clinical use. Segmentations created by the model required few corrections [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Three subcentimeter tumors initially missed by radiologists were detected by the proposed deep learning framework. Upon consultation and review, the radiologist confirmed the lesions as malignant Illustrative cases [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: The highest segmentation accuracy for intrahepatic anatomy of t [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 5
Figure 5. Figure 5: Test set results, with correct, under- (pink) and over-segmentation (blue), and corresponding DSC. Five representative cases were used to highlight the network’s performance in various scenarios: a qualitatively good scan (patient 1), steatotic liver (patient 2), diffe…

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Reviewed August 15, 2026 · model on record in the stance chip above.