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REVIEW 4 major objections 4 minor 34 references

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation

T0 review · 4 major / 4 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read A U-Net built on xLSTM blocks outperforms convolutional, transformer, and Mamba variants on retroperitoneal tumor CT segmentation, and does so with less complexity.

desk verdict The new retroperitoneal tumor dataset is the real contribution, but the reported metrics in Table 1 are internally inconsistent and the ViLU-Net superiority claim rests on a single unvalidated split without error bars or the key xLSTM-UNet baseline. read the letter →

arxiv 2502.00314 v1 pith:LMMPXYZF submitted 2025-02-01 eess.IV cs.CV

Moein Heidari , Ehsan Khodapanah Aghdam , Alexander Manzella , Daniel Hsu , Rebecca Scalabrino , Wenjin Chen , David J. Foran , Ilker Hacihaliloglu This is my paper · ORCID
classification eess.IVcs.CV
keywords RetroperitonealtumorCTimagesegmentationU-NetxLSTMViLU-NetMambaVisionTransformerDeeplearning
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 argues that swapping the core of a U-Net from convolutional, transformer, or Mamba blocks to Vision xLSTM blocks yields both more accurate and cheaper segmentation of retroperitoneal tumors in CT scans. To make the case, the authors introduce a new expert-annotated CT dataset of retroperitoneal tumors (82 cases, 77 after preprocessing) and compare four U-Net-style architectures on it plus the public FLARE abdominal organ dataset. Their proposed model, ViLU-Net, reports the best average Dice Similarity Coefficient (0.9309), Normalized Surface Distance (0.9292), and Intersection over Union (0.8720) and the lowest Hausdorff distance (11.19) on the tumor dataset, ahead of nnU-Net, SwinUNETR, and U-Mamba. The authors further claim the model offers a significant reduction in complexity, which matters because transformer-based segmentation is often too memory-hungry for clinical hardware. If the claim holds, xLSTM blocks would be a practical drop-in upgrade for U-shaped segmentation networks in resource-constrained medical settings.

What carries the argument

The load-bearing component is the ViL (Vision xLSTM) block, built from mLSTM cells whose recurrence is $C_t = f_t C_{t-1} + i_t v_t k_t^\top$, $n_t = f_t n_{t-1} + i_t k_t$, and $\tilde h_t = C_t q_t / \max(|n_t^\top q_t|, 1)$, with hidden state $h_t = o_t \odot \tilde h_t$. Odd-numbered blocks scan patch tokens from top-left to bottom-right and even-numbered blocks scan in reverse, so the network sees long-range context in both directions with linear complexity in sequence length. In ViLU-Net, a convolutional stem with instance normalization and LeakyReLU produces the base feature map, two ViL blocks form each encoder stage, down-samplers halve spatial resolution while doubling channels, and up-samplers plus skip connections restore resolution before a softmax segmentation head. The mLSTM cells are what make the architecture simultaneously parallelizable, long-range, and cheap—the property the paper argues gives xLSTM an edge over ViT and Mamba companions inside U-Net.

What would settle it

Run a 5-fold cross-validation of ViLU-Net, nnU-Net, SwinUNETR, U-Mamba, and xLSTM-UNet on the same 77-case retroperitoneal dataset, with identical preprocessing, the same 300-epoch training schedule, and per-model hyperparameter search for each fold. If ViLU-Net's mean DSC across folds is not higher than nnU-Net's by more than the standard deviation of the fold-wise scores, or if xLSTM-UNet matches it, the claimed superiority is not established.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that a U-shaped segmentation network whose encoder-decoder core is built from Vision xLSTM blocks—called ViLU-Net—dominates three contemporary alternatives on both a new retroperitoneal tumor CT dataset and the FLARE abdominal organ dataset. Against nnU-Net, SwinUNETR, and U-Mamba, ViLU-Net achieves the highest average DSC (0.9309 vs. 0.9013, 0.8310, and 0.7694) and lowest HD (11.19 vs. 24.64, 51.28, and 64.35) on the tumor dataset, with corresponding gains in NSD and IoU. The authors interpret this as evidence that xLSTM's exponential gating and matrix-memory recurrence capture long-range dependencies more effectively than convolutions, self-attention, or selective state spaces, while keeping linear memory cost. They also claim the model is simpler than the alternatives, positioning it as a candidate for deployment where computational resources are limited.

Load-bearing premise

The entire superiority claim rests on a single fixed train/validation split of 77 scans, with no cross-validation, no error bars, no statistical test, and no direct comparison against xLSTM-UNet, the near-identical architecture the model is built from; if hyperparameters were not equally tuned for each baseline or the split is unrepresentative, the ranking in Table 2 could change.

Editorial extensions

If this is right

  • The xLSTM building block can replace transformer and Mamba blocks in U-shaped medical segmentation networks without losing accuracy, since ViLU-Net beats both SwinUNETR and U-Mamba on the tumor dataset.
  • Retroperitoneal tumor segmentation, made hard by irregular shapes and proximity to vital structures, can be automated at a level the paper argues is clinically usable, with Hausdorff distance more than halved relative to the best CNN baseline.
  • The released expert-annotated dataset of 82 retroperitoneal tumor cases gives the community a benchmark for a class of rare tumors where public data are scarce.
  • Because ViLU-Net also leads on the FLARE multi-organ abdomen dataset, the authors' claim is not limited to one tumor type; the same architecture generalizes across segmentation targets.
  • Lower computational complexity than transformer-based alternatives would make accurate segmentation feasible on the limited GPU hardware typical of clinical settings, a claim the paper explicitly makes.

Reading between the lines

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

  • If the reported gains survive proper statistical evaluation, xLSTM blocks could become a standard plug-in for U-Net encoders wherever memory is the bottleneck, not just for retroperitoneal tumors.
  • The omission of xLSTM-UNet as a baseline means the paper has not isolated whether ViLU-Net's advantage comes from its specific modifications or from the xLSTM blocks already present in xLSTM-UNet; a head-to-head comparison would separate the two.
  • Since the ViL block is modality-agnostic, the same architecture could be transferred to MRI or PET tumor segmentation; a natural test would be applying ViLU-Net to a public brain-tumor or prostate MRI benchmark.
  • The paper's claim of reduced complexity could be quantified directly by reporting parameter counts, FLOPs, and memory per case for each baseline; that number is not in the paper and is the key missing evidence for the efficiency claim.
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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 evaluates U-Net variants built from CNN, Vision Transformer, Mamba, and xLSTM components on two CT datasets: a public abdomen multi-organ dataset (FLARE 2022) and a newly introduced in-house retroperitoneal tumor dataset of 82 cases (with 77 used after preprocessing). The authors propose ViLU-Net, a U-shaped architecture that inserts Vision-LSTM (ViL) blocks into the encoder-decoder path, and report that it achieves the highest average DSC, NSD, and IoU and the lowest average HD on both datasets, while also claiming a significant reduction in complexity. The code is publicly released, and the dataset is presented as a contribution.

Significance. If the reported results are robust, the paper would provide two useful contributions: a new expert-annotated retroperitoneal tumor CT dataset, and evidence that an xLSTM/ViL-based U-Net variant can match or outperform transformer- and Mamba-based counterparts at lower computational cost. The study also benchmarks several recent architectures on the same protocol, which is informative for practitioners. The public code is a concrete strength. However, the headline superiority claim is supported only by single-split metric averages with no variance or significance testing, and the efficiency claim is not quantified, so the significance at this stage is mainly as a dataset release and preliminary benchmark rather than a rigorously established architectural finding.

major comments (4)
  1. [§3.2.2, Table 2] The central claim that ViLU-Net 'demonstrates superior performance' on the retroperitoneal dataset rests on a single train/validation split with one DSC/NSD/HD/IoU value per method and no error bars, confidence intervals, cross-validation, or statistical significance tests. With only 77 cases, the 0.03 DSC gap over nnU-Net (0.9309 vs 0.9013) could easily arise from the choice of split. The authors should report k-fold cross-validation or at least bootstrapped confidence intervals and a paired significance test (e.g., Wilcoxon signed-rank or paired bootstrap) across cases to demonstrate that the improvement is not noise.
  2. [§2 and §3.2.2] ViLU-Net is described as 'akin to xLSTM-UNet' and built from the same ViL blocks, yet xLSTM-UNet (reference [30], the direct architectural predecessor) is never evaluated on either dataset. Because ViLU-Net differs from xLSTM-UNet mainly in block arrangement and integration details, omitting this baseline means the comparison cannot isolate the contribution of the proposed modification. The xLSTM-UNet baseline should be included under the same training and evaluation protocol.
  3. [§1 and §5] The paper claims 'a significant reduction in complexity' and 'reduced complexity' (Abstract, Introduction, Conclusion) but provides no quantitative measure of efficiency anywhere: no parameter counts, FLOPs, memory usage, training time, or inference latency are reported for ViLU-Net or any baseline. This claim is currently unsupported and should be substantiated with concrete complexity measurements for all compared methods.
  4. [§3.1, Table 1] The abdomen CT results contain a mathematically impossible relationship between DSC and IoU: for the same binary segmentation, IoU is always less than or equal to DSC, yet Table 1 reports DSC 0.8469 and IoU 0.9662 for nnU-Net (similarly for other rows). This indicates that the IoU values are either computed on different classes/sets (e.g., averaged over all classes rather than foreground only) or are otherwise inconsistent with the DSC computation. The metric definitions and averaging procedure must be clarified and corrected, because this casts doubt on the correctness of all reported metrics in both tables.
minor comments (4)
  1. [Abstract and §3.2.1] The number of cases is inconsistent: the Abstract and §3.2 state the dataset has 82 cases, but §3.2.1 says 85 initial cases with 8 excluded, yielding 77 used cases. This arithmetic (85-8=77) does not match 82; please clarify whether the dataset comprises 82 or 77 cases, or state the number of included and excluded cases precisely.
  2. [§2.2] The ViLU-Net architecture hyperparameters are not specified: the number of ViL blocks at each resolution, channel widths, patch size, and downsampling factors are absent. Figure 1 shows a schematic, but a table or explicit configuration is needed for reproducibility, especially since the implementation is based on nnU-Net's framework which may set some of these automatically.
  3. [Introduction, §1] There are several typos and duplicated words, e.g., 'andreliantonextensive' (missing spaces) and 'the the Mamba-based method' in §3. A careful proofreading pass is needed.
  4. [§3.2.2] The qualitative claims in the text (e.g., 'more robust and smoother segmentations', 'minimal false positives') refer to Figure 3, but the figure is not referenced with panel-specific descriptions that quantify what is being seen; adding segmentation overlays with arrows and a discussion of failure cases would strengthen the presentation.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is an empirical benchmark whose reported scores are measured outcomes, not quantities derived from fitted inputs or self-citation chains.

full rationale

This paper makes no theoretical derivation or prediction that could reduce to its own inputs. ViLU-Net is assembled from published components (ViL blocks, U-Net skeleton) exactly as stated, and reusing prior art from the xLSTM and ViL literature is normal construction, not circularity. The central evidence is Table 2, where DSC, NSD, HD, and IoU are measured against expert ground-truth masks under an empirical evaluation protocol; no fitted constant, normalization choice, or calibration curve is renamed as a prediction. Several methodological weaknesses exist — the near-identical xLSTM-UNet baseline is omitted, no cross-validation or statistical tests are reported, and the claimed complexity reduction is not quantified — but those are evidentiary and reproducibility concerns, not circularity of derivation. Self-citations in the references are surveys and background material and are not load-bearing for the performance comparison. Therefore the circularity score is 0.

Assumptions & free parameters 1 free parameters · 3 assumptions · 1 invented entities

The work is an empirical architecture comparison, so the ledger contains no fitted constants in the statistical sense. The main assumptions are that the nnU-Net training protocol is fair across architectures, that expert annotations are trustworthy, and that case exclusions are harmless. The only invented entity is the ViLU-Net architecture itself, which is a reconfiguration of known components.

free parameters (1)
  • ViLU-Net architecture hyperparameters (number of ViL blocks, channel widths, downsampling factors)
    Hand-chosen following xLSTM-UNet and U-Mamba conventions; no ablation or sensitivity analysis is reported to justify these choices.
assumptions (3)
  • domain assumption The nnU-Net framework provides a fair and standard training protocol for all compared methods.
    All models were trained within the nnU-Net pipeline (Section 3, Training and implementation procedures); if the framework favors certain architectures, the comparison could be biased.
  • domain assumption Expert radiologist annotations are accurate ground truth for tumor boundaries.
    The in-house dataset relies on manual annotations by experienced physicians (Section 3.2); annotation variability is not assessed.
  • ad hoc to paper Excluding 8 of 85 cases for technical issues does not bias the results.
    Section 3.2.1 reports exclusion of cases with incorrect orientation, flawed scanning, or missing masks; the characteristics of excluded cases are not compared with included ones, so selection bias cannot be ruled out.
invented entities (1)
  • ViLU-Net architecture independent evidence
    purpose: Proposed U-Net variant that uses Vision LSTM (ViL) blocks in the encoder and decoder for efficient long-range dependency modeling in segmentation.
    The model is fully described and code is said to be public, so others can evaluate it; however, it is a composition of existing ViL and U-Net building blocks rather than a new mechanism.

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Cite this review

Pith. "Pith review of A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation." pith.science (2026). https://pith.science/paper/LMMPXYZF

@misc{pith2026250200314,
  author       = {Pith},
  title        = {Pith review of: A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LMMPXYZF}},
  note         = {Machine review of arXiv:2502.00314}
}
read the original abstract

The retroperitoneum hosts a variety of tumors, including rare benign and malignant types, which pose diagnostic and treatment challenges due to their infrequency and proximity to vital structures. Estimating tumor volume is difficult due to their irregular shapes, and manual segmentation is time-consuming. Automatic segmentation using U-Net and its variants, incorporating Vision Transformer (ViT) elements, has shown promising results but struggles with high computational demands. To address this, architectures like the Mamba State Space Model (SSM) and Extended Long-Short Term Memory (xLSTM) offer efficient solutions by handling long-range dependencies with lower resource consumption. This study evaluates U-Net enhancements, including CNN, ViT, Mamba, and xLSTM, on a new in-house CT dataset and a public organ segmentation dataset. The proposed ViLU-Net model integrates Vi-blocks for improved segmentation. Results highlight xLSTM's efficiency in the U-Net framework. The code is publicly accessible on GitHub.

Figures

Figures reproduced from arXiv: 2502.00314 by the authors.

Figure 1
Figure 1. (a) Schematic representation of the proposed method, ViLU-Net, (b) the ViL block, (c) convolutional stem, and [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Visualized segmentation examples of abdominal organ segmentation in CT. The ViLU-Net excels at differenti [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Visual comparisons of different methods on our in house dataset. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Sample visualization of our in-house dataset from the 3 different views along with the corresponding segmentation [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

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Reference graph

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    2023 arXiv

Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.