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REVIEW 5 major objections 5 minor 164 references

ConfLUNet: Multiple sclerosis lesion instance segmentation in presence of confluent lesions

T0 review · 5 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read ConfLUNet, the first end-to-end instance segmentation framework for MS lesions, significantly outperforms connected components and automated confluent lesion splitting on the held-out test set in panoptic quality, lesion detection F1, and…

desk verdict A solid, transparent instance-segmentation paper for MS lesions whose main risk is the acknowledged single-rater ground truth, but which deserves peer review. read the letter →

arxiv 2505.22537 v1 pith:MKGONOT3 submitted 2025-05-28 eess.IV

Maxence Wynen , Pedro M. Gordaliza , Maxime Istasse , Anna Stölting , Pietro Maggi , Benoît Macq , Meritxell Bach Cuadra This is my paper
classification eess.IV
keywords multiplesclerosisinstancesegmentationconfluentlesionsMRIFLAIRpanopticqualitylesiondetectiondeeplearning
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 multiple sclerosis (MS) lesion analysis on MRI should be evaluated and built as instance segmentation, not just semantic segmentation, because a single connected 'confluent' lesion can contain several distinct disease units and standard post-processing cannot separate them. The authors formalize confluent lesions, define their constituent confluent lesion units (CLUs) and an extended CLU+ definition, and introduce CLU-aware precision, recall, and F1 metrics. On top of this, they present ConfLUNet, described as the first end-to-end instance segmentation framework for MS lesions, which jointly predicts a semantic lesion map, a lesion center heatmap, and voxel-to-center offset vectors from a single FLAIR image. On a held-out test set of 13 patients, ConfLUNet significantly surpasses connected components (CC) and automated confluent lesion splitting (ACLS) in Panoptic Quality (42.0% vs 37.5% and 36.8%) and detection F1 (67.3% vs 61.6% and 59.9%), and achieves the highest CLU detection F1 (81.5%). If correct, this shows that jointly optimizing detection and delineation can resolve lesion units that post-processing either merges or oversplits, and it gives the field a metric framework to measure that.

What carries the argument

The argument rests on two pieces of machinery. One is formal definition: a confluent lesion is a connected component that overlaps at least two reference lesion instances, and each of those instances is a confluent lesion unit (CLU); dilating the semantic mask before the same test defines extended CLU+, capturing near-touch lesions. These definitions convert a clinically messy phenomenon into countable objects that can be scored. The other is ConfLUNet's architecture, which adds two output heads to a standard self-configuring 3D encoder-decoder segmentation backbone: a heatmap of lesion centers (Gaussian-smoothed centers of mass) and a 3D offset vector field pointing from each voxel to its lesion center. At inference, voxels are displaced by their predicted offsets and assigned to the nearest detected center, so lesion boundaries reflect learned geometric evidence rather than pure spatial connectivity.

What would settle it

Re-annotate the 13 test-set patients with several independent raters who each mark CLU instances inside confluent regions, then recompute ConfLUNet's CLU detection F1 against each rater and against a multi-rater consensus reference; if ConfLUNet's advantage over connected components shrinks or disappears under these alternative references, the reported gains are an artifact of a single annotator's judgment rather than a property of the method.

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Extended reading notes

Core claim

On its own terms, the paper's central claim is that an end-to-end model that simultaneously learns what is lesion, where lesion centers are, and which voxel belongs to which center can separate confluent MS lesion units better than deriving instances from a semantic mask by connected components or by statistical splitting. The evidence is the held-out comparison: ConfLUNet reaches Panoptic Quality 42.0% against 37.5% for CC and 36.8% for ACLS (p = 0.017 and 0.005), lesion-wise F1 67.3% against 61.6% and 59.9% (p = 0.028 and 0.013), and CLU F1 81.5%, with statistically significant recall gains over CC (+12.5%) and precision gains over ACLS (+31.2%). The paper also establishes the failure modes of the existing methods: CC systematically undercounts CLUs while ACLS systematically oversplits lesions, and these behaviors hold across three different semantic segmentation tools, supporting the claim that the default evaluation practice is misaligned with clinical needs.

Load-bearing premise

The whole evaluation stands on the single-rater manual annotations being a correct partition of confluent lesions into units, yet the paper itself acknowledges that in large confluent lesions distinguishing individual units is extremely difficult, if not impossible, for human raters.

Editorial extensions

If this is right

  • Connected components, the default post-processing across the MS lesion literature, systematically underestimates confluent lesion unit counts and should be reconsidered for instance-level evaluation.
  • ACLS overestimates lesion counts and oversplits, so its high recall comes at a precision cost that the new metrics expose.
  • ConfLUNet offers a balanced precision-recall operating point, which the authors argue is closer to clinical needs for lesion counting and lesion-level biomarkers.
  • The formal CLU and CLU+ definitions and metrics give future work a shared reference for measuring instance segmentation in MS, replacing the current reliance on CC-derived counts.
  • Because ConfLUNet needs only a single FLAIR volume, the approach is a practical candidate for clinical pipelines that do not routinely acquire research-grade multi-contrast scans.

Reading between the lines

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

  • If the single-rater ground truth is noisy in exactly the confluent regions the method targets, the reported advantage might change in either direction; a multi-rater study would tell whether ConfLUNet is exploiting signal or label bias.
  • The CLU+ dilation-based definition effectively forgives boundary disagreements of one voxel, which suggests the practical near-term role of such a model may be to flag candidate confluent regions and their units for human confirmation.
  • The same center-and-offset formulation could be tested on longitudinal data, since the authors note that temporal appearance can disambiguate confluent lesions; that would be a natural and testable extension for CLU separation.
  • Reporting patient-wise averages hides the clinical worst case; the lesion-wise analysis in the paper shows CC's CLU recall falling to 41.5%, implying future evaluations should report both patient-wise and lesion-wise CLU metrics.
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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

5 major / 5 minor

Summary. The paper addresses the problem of instance segmentation of multiple sclerosis (MS) lesions, with a focus on confluent lesions that appear as merged clusters on MRI. The authors formalize definitions of confluent lesions and confluent lesion units (CLUs), propose CLU-aware evaluation metrics, systematically compare two existing post-processing approaches (connected components, CC, and automated confluent lesion splitting, ACLS), and introduce ConfLUNet, an end-to-end 3D instance segmentation network with semantic, center, and offset heads. On a held-out test set of 13 patients, ConfLUNet reportedly achieves significantly higher Panoptic Quality (42.0% vs 37.5%/36.8%) and lesion detection F1 (67.3% vs 61.6%/59.9%) than CC and ACLS, and the highest F1CLU (81.5%). The paper also provides open-source code and a detailed evaluation framework.

Significance. If the results hold, the paper makes a valuable contribution by formalizing the CLU evaluation problem, providing reproducible metrics, and demonstrating that an end-to-end model can achieve a better precision-recall balance than connectivity-based or center-splitting post-processing. The experimental design is careful in several respects: a held-out test set, 5-fold cross-validation, an architecture-matched baseline (3D U-Net with the same backbone), paired Wilcoxon tests, Holm-Bonferroni-corrected analyses, and explicit disclosure of limitations. The release of code and containers supports reproducibility. The main significance is in the evaluation framework and in establishing a concrete baseline for future instance segmentation methods in MS. However, the strength of the central claim is tempered by the heavy reliance on single-rater manual annotations for confluent regions, which the paper itself acknowledges are extremely difficult to label, and by the small test set with very few confluent cases.

major comments (5)
  1. [Section 4.1.2, Appendix A, Limitations] The evaluation rests entirely on manual instance annotations made by a single neurobiologist and reviewed by a single neurologist. The paper itself states in Appendix A (Fig. A.10) that for large confluent lesions it is 'extremely difficult, if not impossible, to distinguish individual lesion units,' and the Limitations section concedes that 'single-rater annotations may introduce bias.' Because the reference instances define the CLU sets L_CLU and L_CLU+ (Eqs. 3–4) and the center/offset training targets (Section 3.1), all reported metrics (PQ, F1, F1CLU) and the ranking of ConfLUNet versus CC/ACLS inherit this potential bias. Without inter-rater reproducibility data or at least a sensitivity analysis on a subset of images re-annotated by a second rater, the central claim that ConfLUNet better separates true confluent lesion units is not fully established.
  2. [Section 5.3, Table 5] The paper highlights F1CLU as a key result, but the difference between ConfLUNet (81.5%) and CC (79.8%) is not reported as statistically significant, and the F1CLU+ improvement over CC is marginal (p = 0.046) and may not survive the Holm-Bonferroni correction shown in Appendix D. The CLU-specific comparisons are further limited to the 7 test patients with at least two CLUs (Section 5.3), giving very low statistical power. The claims about improved CLU detection should be softened accordingly, and the exact p-values (including non-significant ones) for the F1CLU comparisons should be reported.
  3. [Section 4.1.1, Section 5.3] The test set is small (n=13, with only 7 patients exhibiting at least two CLUs), and the two patients with the most extreme confluency were excluded by design because their confluent lesions were deemed indiscernible. Excluding the cases where the ground truth is most ambiguous removes exactly the cases most relevant to the method's intended use case, which narrows the external validity of the reported gains. The authors acknowledge this in the Limitations, but they do not quantify how the conclusions would change if those two patients were included (even with their uncertain labels). Reporting performance on those two patients as a sensitivity analysis would help assess robustness.
  4. [Abstract, Section 3, Reference [21]] The abstract and Section 3 describe ConfLUNet as 'the first end-to-end instance segmentation framework for MS lesions,' but the authors' own prior work, reference [21] (ISBI 2024), already proposes ConfLUNet for confluent lesion identification. The present manuscript should explicitly state the architectural and methodological differences from [21] and adjust the novelty claim accordingly. As written, the 'first' claim is at odds with the cited prior work and could be misleading to readers.
  5. [Section 4.2, FPCLU definition] The construction of FPCLU (predicted lesions that match a reference lesion but are not the best match for that reference) implies that any method that never oversplits, such as CC, will automatically achieve PrecisionCLU = 100% regardless of how many reference CLUs it merges. Table 5 confirms that CC reaches 100% PrecisionCLU. As a result, F1CLU comparisons between CC and splitting methods are essentially recall-only comparisons, and the proposed metric does not directly penalize the merging failure mode that motivates the paper. The authors should justify this asymmetry or introduce an additional metric that penalizes merging explicitly.
minor comments (5)
  1. [Abstract] There is a formatting error in the abstract: 'textF 1CLU' should be 'F1CLU'.
  2. [Throughout] The capitalization of the method name is inconsistent: the text uses both 'ConfLUNet' and 'ConfLUnet' (e.g., Figure D.12), and 'ConfLUnet' appears in some figures and tables. The authors should standardize to one spelling.
  3. [Section 4.3, Reference list] The text cites 'SAMSEG [114]' for the SAMSEG tool, but the main reference list contains only [1]–[47]; [114] appears in the Appendix E reference list (Cerri et al.). This citation mismatch should be fixed so that the main text uses the correct reference number.
  4. [Section 2.1, References] The ACLS description cites '[20, 120]' and 'our previous work [29]', but [120] is only in the Appendix E bibliography, not in the main reference list. The authors should ensure all citations in the main text point to the main reference list or move the relevant entries.
  5. [Section 4.2] The matching procedure uses a low IoU threshold (λ = 0.1) and mutual-best matching. The authors should briefly justify why this threshold is appropriate for 3D lesions and discuss its impact on reported PQ and F1 values, given that small spatial errors can cause a predicted lesion to fall below the threshold.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: ConfLUNet's performance claims rest on held-out test comparisons, and the CLU definitions/metrics are evaluation instruments rather than fitted inputs.

full rationale

The paper's central claims are empirical: ConfLUNet is trained on 50 patients and evaluated on a held-out set of 13 patients against CC and ACLS, with PQ, F1, and CLU-aware metrics computed from test-set predictions. No equation in the paper reduces a reported prediction to the input by construction. The reference centers used to supervise the center/offset heads (Section 3.1) are derived from the manual instance annotations, but this is standard supervised training; the held-out split means the reported numbers are not fitted. The CLU and CLU+ definitions (Eqs. 2-4) are used to define evaluation sets and metrics, not to generate ConfLUNet's outputs, and validation-based model selection used panoptic quality (Section 3.2), not CLU-specific metrics, so no target metric was directly optimized. Self-citations to the authors' prior ConfLUNet work [21] and ISMRM comparison [29] are motivational and are re-validated on new data here; no load-bearing argument depends on an unverified self-cited theorem. The acknowledged limitation of single-rater annotations (Section 6, Appendix A) is a validity and generalizability concern about the ground truth, not a circularity in the derivation chain: even if the labels are imperfect, the methods are compared against the same labels in an externally falsifiable way. No claim reduces to its own assumptions by definition or by self-citation, so the appropriate circularity score is 0.

Assumptions & free parameters 7 free parameters · 5 assumptions · 0 invented entities

The central claim rests primarily on the reliability of manual instance annotations and the cross-sectional visibility of CLUs. Seven hyperparameters (thresholds, dilation, sigma, loss weights, connectivity choices) affect the reported metrics. No new physical entities or mediators are introduced.

free parameters (7)
  • Matching IoU threshold lambda = 0.1
    Used in Eq. 5 to define lesion matches. Directly determines TP/FP/FN and therefore PQ, F1, and CLU metrics. Chosen to accommodate 3D variability, not derived from data.
  • Semantic probability threshold = 0.5
    Binarization threshold for semantic maps (Sec. 3.3 and 4.3). Affects all downstream instance masks and metrics.
  • Size filter for predicted lesions = <3mm along any axis or <14mm3
    Applied to remove small predicted instances (Sec. 3.3, based on clinical definition [32]). Strongly affects lesion counts and CLU metrics.
  • Dilation iterations for CLU+ definition = 1
    Single-iteration binary dilation defines extended confluent lesions (Sec. 2, Eq. 4). The choice of one iteration is ad hoc and affects CLU+ metrics.
  • Center heatmap Gaussian sigma = 2 voxels
    Controls center detection specificity in ConfLUNet (Sec. 3.1).
  • Loss weights alpha and beta = alpha=100, beta=1
    Weights for center and offset losses in Ltotal (Sec. 3.2). Chosen to balance magnitudes; influences training and final instance quality.
  • Connectivity structures for CC and ACLS = 6-connectivity for CC; 26-connectivity for ACLS center detection
    Evaluation choices in Sec. 4.3 that affect how instances are formed and counted.
assumptions (5)
  • domain assumption Manual instance annotations are a reliable ground truth for CLUs despite the difficulty of separating confluent lesions.
    Section 4.1.2 and Appendix A. Fig. A.10 states confluent lesions can be 'extremely difficult, if not impossible' to distinguish; if the single-rater annotations are unreliable, all metrics and comparisons inherit that error.
  • domain assumption CLUs are visible in a single cross-sectional FLAIR image (with MPRAGE/EPI consultation for raters); temporal information is not required.
    The Introduction notes longitudinal scans could help separate lesions but are expensive; the method uses a single FLAIR. If some CLUs are indistinguishable cross-sectionally, the task is ill-posed for those cases.
  • domain assumption Each lesion has a well-defined center of mass that serves as the instance anchor; learning offset vectors to this center is sufficient to separate confluent lesions.
    Sec. 3.1 center and offset heads. This is the architectural assumption inherited from bottom-up instance segmentation (Panoptic DeepLab). It may fail for highly irregular or elongated lesions.
  • domain assumption Connected component analysis on reference semantic masks (Eq. 2) and the dilation-based CLU+ definition capture the clinically meaningful notion of confluence.
    Sec. 2 formal definitions. These definitions are the paper's own choices; the extended CLU+ via one dilation is ad hoc.
  • standard math Standard connected components, Gaussian filtering, and IoU computations behave as expected.
    Used throughout the method and evaluation; no proof needed.

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Cite this review

Pith. "Pith review of ConfLUNet: Multiple sclerosis lesion instance segmentation in presence of confluent lesions." pith.science (2026). https://pith.science/paper/MKGONOT3

@misc{pith2026250522537,
  author       = {Pith},
  title        = {Pith review of: ConfLUNet: Multiple sclerosis lesion instance segmentation in presence of confluent lesions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MKGONOT3}},
  note         = {Machine review of arXiv:2505.22537}
}
read the original abstract

Accurate lesion-level segmentation on MRI is critical for multiple sclerosis (MS) diagnosis, prognosis, and disease monitoring. However, current evaluation practices largely rely on semantic segmentation post-processed with connected components (CC), which cannot separate confluent lesions (aggregates of confluent lesion units, CLUs) due to reliance on spatial connectivity. To address this misalignment with clinical needs, we introduce formal definitions of CLUs and associated CLU-aware detection metrics, and include them in an exhaustive instance segmentation evaluation framework. Within this framework, we systematically evaluate CC and post-processing-based Automated Confluent Splitting (ACLS), the only existing methods for lesion instance segmentation in MS. Our analysis reveals that CC consistently underestimates CLU counts, while ACLS tends to oversplit lesions, leading to overestimated lesion counts and reduced precision. To overcome these limitations, we propose ConfLUNet, the first end-to-end instance segmentation framework for MS lesions. ConfLUNet jointly optimizes lesion detection and delineation from a single FLAIR image. Trained on 50 patients, ConfLUNet significantly outperforms CC and ACLS on the held-out test set (n=13) in instance segmentation (Panoptic Quality: 42.0% vs. 37.5%/36.8%; p = 0.017/0.005) and lesion detection (F1: 67.3% vs. 61.6%/59.9%; p = 0.028/0.013). For CLU detection, ConfLUNet achieves the highest F1[CLU] (81.5%), improving recall over CC (+12.5%, p = 0.015) and precision over ACLS (+31.2%, p = 0.003). By combining rigorous definitions, new CLU-aware metrics, a reproducible evaluation framework, and the first dedicated end-to-end model, this work lays the foundation for lesion instance segmentation in MS.

Figures

Figures reproduced from arXiv: 2505.22537 by the authors.

Figure 1
Figure 1. Illustration of key concepts formalized in this study. (a) Axial FLAIR image; [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Illustration of ConfLUNet pipeline. 3D FLAIR patches (a) are provided as input [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Histograms of lesion and CLU(+) distributions in the dataset. [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Examples of the mutually consistent lesion matching strategy and of [PITH_FULL_IMAGE:figures/full_fig_p013_4.png]
Figure 5
Figure 5. Figure 5: Box plots of the patient-wise metrics obtained for the three lesion segmentation [PITH_FULL_IMAGE:figures/full_fig_p016_5.png]
Figure 6
Figure 6. Figure 6: Bland-Altman plots for predicted and reference lesion/CLU/CLU+ counts and [PITH_FULL_IMAGE:figures/full_fig_p017_6.png]
Figure 7
Figure 7. Figure 7: Box plots of the patient-wise general metrics obtained for the three models. The [PITH_FULL_IMAGE:figures/full_fig_p020_7.png]
Figure 8
Figure 8. Figure 8: Bland-Altman plot for predicted and reference lesion/CLU/CLU+ counts. Com [PITH_FULL_IMAGE:figures/full_fig_p022_8.png]
Figure 9
Figure 9. Figure 9: Illustrative examples of predictions made by connected components (CC), auto [PITH_FULL_IMAGE:figures/full_fig_p023_9.png]

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Pith tools

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