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arxiv: 2604.23187 · v1 · submitted 2026-04-25 · 💻 cs.CV · cs.AI

Recognition: unknown

DyABD: The Abdominal Muscle Segmentation in Dynamic MRI Benchmark

Aonghus Lawlor, Catherine Masson, David Bendahan, Kathleen M. Curran, Niamh Belton, Thierry Bege, Victoria Joppin

Pith reviewed 2026-05-08 08:40 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords dynamic MRIabdominal muscle segmentationherniabenchmark datasetmedical image segmentationDice coefficientgeneralizationpre-post surgery
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The pith

DyABD is the first benchmark dataset for abdominal muscle segmentation in dynamic MRIs of exercising hernia patients, where most models reach only 0.82 Dice.

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

This paper presents DyABD, a new dataset of dynamic abdominal magnetic resonance images taken while patients with hernias perform exercises, complete with detailed annotations of the abdominal muscles. The dataset stands out because it captures extreme anatomical changes from movement, includes both pre- and post-surgery scans, and targets research on hernia recurrence. The authors test a range of existing segmentation models under supervised, few-shot, and zero-shot conditions using this previously unseen data. They report that the majority of techniques attain a Dice coefficient of 0.82, which points to substantial remaining challenges in handling real clinical variability in medical image segmentation.

Core claim

DyABD establishes the first annotated collection of dynamic abdominal MRIs acquired during various exercises in patients with abdominal hernias, encompassing both pre- and post-corrective surgery scans, and through systematic testing shows that current segmentation models from different learning paradigms attain an average Dice coefficient of 0.82 on this unseen benchmark.

What carries the argument

The DyABD benchmark dataset, which defines the abdominal muscle segmentation task on dynamic MRIs featuring high anatomical variability from exercise and pre/post surgery conditions.

If this is right

  • Clinical studies of abdominal hernia recurrence can now incorporate quantitative segmentation of muscle structures from dynamic scans.
  • Segmentation models require evaluation on dynamic data with extreme anatomical variation to prove real-world generalization.
  • The 0.82 Dice ceiling on this benchmark indicates that progress metrics based on static images overstate capability for motion-heavy cases.
  • Pre- and post-surgery image pairs enable direct measurement of muscle changes after corrective procedures.
  • Future medical imaging benchmarks should include similar exercise-induced variability to reflect practical diagnostic conditions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Accurate dynamic segmentation could improve surgical planning by revealing how muscles deform under load in hernia patients.
  • The dataset format may encourage parallel benchmarks for other moving anatomy such as cardiac or respiratory structures.
  • Persistent gaps in zero-shot performance suggest that transfer from static MRI datasets is insufficient without motion-aware architectures.

Load-bearing premise

The high-quality annotations accurately and consistently mark abdominal muscles across all frames of the dynamic sequences despite motion-induced distortions, and the patient cohort with chosen exercises represents typical clinical cases.

What would settle it

Independent experts re-annotating a held-out subset of images and reporting inter-annotator Dice agreement below 0.9, or a new segmentation method achieving sustained Dice scores above 0.90 on the full DyABD test set.

read the original abstract

This work introduces DyABD, a novel and complex benchmark dataset of dynamic abdominal MRIs from patients with abdominal hernias and associated high quality abdominal muscle annotations. DyABD is the first-of-its-kind in four key ways; (1) it proposes the first abdominal muscle segmentation task, (2) the dynamic MRIs are acquired whilst the patients perform various exercises, introducing extreme anatomical variability, making it one of the most challenging segmentation datasets to date, (3) it includes both pre and post corrective MRIs and (4) DyABD promotes clinical research into the high recurrence rates of abdominal hernias. Beyond dataset introduction, this work provides a comprehensive evaluation of the generalisation capabilities of existing segmentation models across Supervised, Few Shot and Zero Shot paradigms on the unseen DyABD dataset. This work reveals that there is still room for substantial improvement in the field of medical image segmentation, with the majority of techniques achieving a Dice Coefficient of 0.82. This work therefore sheds light on the true progress of the field and redefines the benchmark for progress in medical image segmentation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. This manuscript introduces DyABD, a benchmark dataset of dynamic abdominal MRI scans from patients with abdominal hernias, acquired during various exercises and including both pre- and post-corrective surgery images, along with annotations for abdominal muscle segmentation. It claims to be the first such dataset in four respects: defining the abdominal muscle segmentation task, capturing extreme anatomical variability from dynamic exercise imaging, including pre/post scans, and enabling clinical research on hernia recurrence rates. The paper also reports a comprehensive evaluation of existing segmentation models under supervised, few-shot, and zero-shot paradigms on held-out DyABD data, concluding that most techniques achieve a Dice coefficient of 0.82 and that substantial room for improvement remains in medical image segmentation.

Significance. If the annotations are shown to be reliable and the dataset representative of clinical variability, DyABD could serve as a valuable, challenging benchmark for advancing segmentation methods that handle dynamic anatomical changes in MRI, with direct relevance to improving understanding of abdominal hernia recurrence. The evaluation across multiple learning paradigms is a positive step toward assessing generalization. However, the lack of supporting details on annotation quality and experimental protocols currently limits the strength of the claim that current methods have reached a performance ceiling.

major comments (2)
  1. Dataset description: The central claim that DyABD supplies 'high quality abdominal muscle annotations' for an extremely variable dynamic task is load-bearing for both the benchmark value and the reported performance ceiling (Dice 0.82). The manuscript describes acquisition during exercises and pre/post scans but supplies no quantitative evidence on the annotation process: number of raters, training, slice-by-slice vs. propagated labels, or agreement statistics (e.g., mean Dice or surface distance between independent annotations on the same dynamic sequence). Without these, it is impossible to separate model failure from label noise in the reported generalization results.
  2. Evaluation and results: The abstract and main text describe a comprehensive evaluation across supervised, few-shot, and zero-shot paradigms on unseen data, yet supply no details on model architectures, training protocols, data splits, or statistical tests. This makes it unclear whether the 0.82 Dice figure fairly represents current capabilities or whether the conclusion of 'substantial room for improvement' is supported by reproducible evidence.
minor comments (1)
  1. Abstract: The claim that DyABD is 'one of the most challenging segmentation datasets to date' would be strengthened by a brief comparison to existing dynamic or abdominal MRI benchmarks in terms of variability metrics.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments on our manuscript. We address each major comment point by point below, agreeing where the manuscript requires additional detail and outlining the revisions we will make.

read point-by-point responses

  1. Referee: Dataset description: The central claim that DyABD supplies 'high quality abdominal muscle annotations' for an extremely variable dynamic task is load-bearing for both the benchmark value and the reported performance ceiling (Dice 0.82). The manuscript describes acquisition during exercises and pre/post scans but supplies no quantitative evidence on the annotation process: number of raters, training, slice-by-slice vs. propagated labels, or agreement statistics (e.g., mean Dice or surface distance between independent annotations on the same dynamic sequence). Without these, it is impossible to separate model failure from label noise in the reported generalization results.

    Authors: We agree that quantitative evidence on the annotation process is necessary to support the claim of high-quality annotations and to enable readers to distinguish between model limitations and potential label noise. The current manuscript describes the acquisition protocol and pre/post scans but does not include the requested quantitative details. In the revised manuscript, we will add a dedicated subsection on annotation methodology that specifies the number of expert raters, their training, whether annotations were performed slice-by-slice or with temporal propagation, and inter-rater agreement statistics (including mean Dice and surface distance on a sampled subset of dynamic sequences). revision: yes

  2. Referee: Evaluation and results: The abstract and main text describe a comprehensive evaluation across supervised, few-shot, and zero-shot paradigms on unseen data, yet supply no details on model architectures, training protocols, data splits, or statistical tests. This makes it unclear whether the 0.82 Dice figure fairly represents current capabilities or whether the conclusion of 'substantial room for improvement' is supported by reproducible evidence.

    Authors: We acknowledge that the manuscript would be strengthened by explicit details on the experimental setup to support reproducibility and the performance claims. While the paper reports results across the three paradigms and states the average Dice score, it does not provide the specific model architectures, training protocols, data split information, or statistical tests. In the revision, we will expand the evaluation section to include these elements: the exact architectures evaluated, training hyperparameters and protocols, the precise train/validation/test splits on DyABD, and results of any statistical significance tests. This will better substantiate the conclusion that substantial room for improvement remains. revision: yes

Circularity Check

0 steps flagged

No circularity: new dataset introduction with standard empirical evaluation on held-out data

full rationale

The manuscript introduces DyABD as a new benchmark dataset of dynamic abdominal MRIs with annotations and evaluates existing segmentation models (supervised, few-shot, zero-shot) using conventional metrics such as Dice coefficient. No equations, derivations, fitted parameters, or predictions appear in the provided text. Claims of novelty rest on dataset collection details (exercise-acquisition, pre/post scans) rather than any self-referential logic. Reported Dice scores of ~0.82 are direct empirical measurements on unseen data, not reductions to inputs by construction. Annotation quality is asserted but not quantitatively verified in the excerpt; this is a limitation of evidence strength, not circularity in any derivation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

This is an empirical dataset and benchmark paper; it introduces no new mathematical derivations, fitted parameters, or postulated entities beyond standard practices in medical image segmentation evaluation.

axioms (1)
  • domain assumption Dice coefficient is an appropriate and sufficient metric for assessing segmentation performance in this domain.
    Invoked when reporting that the majority of techniques achieve 0.82 Dice.

pith-pipeline@v0.9.0 · 5506 in / 1311 out tokens · 61952 ms · 2026-05-08T08:40:24.694353+00:00 · methodology

discussion (0)

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