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REVIEW 4 major objections 4 minor 1 cited by

CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read The paper claims that comprehensive, standardized training data — 22,022 CT volumes labeled for 167 structures — rather than architectural innovation, is what advances whole-body CT segmentation, and supports this with a model that beats…

desk verdict Valuable dataset release, but the headline performance advantage over TotalSegmentator is not established because the evaluation is curated and partly development-involved. read the letter →

arxiv 2507.22953 v1 pith:2AJN54YD submitted 2025-07-29 eess.IV cs.CV

Murong Xu , Tamaz Amiranashvili , Fernando Navarro , Maksym Fritsak , Ibrahim Ethem Hamamci , Suprosanna Shit , Bastian Wittmann , Sezgin Er
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Sebastian M. Christ Ezequiel de la Rosa Julian Deseoe Robert Graf Hendrik Möller Anjany Sekuboyina Jan C. Peeken Sven Becker Giulia Baldini Johannes Haubold Felix Nensa René Hosch Nikhil Mirajkar Saad Khalid Stefan Zachow Marc-André Weber Georg Langs Jakob Wasserthal Mehmet Kemal Ozdemir Andrey Fedorov Ron Kikinis Stephanie Tanadini-Lang Jan S. Kirschke Stephanie E. Combs Bjoern Menze
This is my paper
classification eess.IVcs.CV
keywords whole-bodyCTsegmentationpseudo-labelinganatomicaldatasetnnU-Netsemi-supervisedlearningorgan-at-riskdelineationdata-centricAIradiationoncology
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 the bottleneck in whole-body CT segmentation is training data, not network architecture, and that a large, heterogeneous, consistently labeled collection can push a standard model past specialized and prior whole-body systems. To show this, the authors assemble the CADS-dataset: 22,022 CT volumes from more than 40 sources across 16 countries, with labels for 167 anatomical structures, roughly 18 times more scans and 60% more targets than the previous standard collection. Because only about one in five scans came with existing annotations, the rest were generated through an automated pseudo-labeling pipeline with shape-based quality control and structure-specific label selection. On the 119 structures shared with the TotalSegmentator baseline, the CADS-model trained with the same nnU-Net family reaches a mean Dice of 90.52% versus 88.09% for the baseline, and it adds 48 previously unsupported structures. Validation across 18 public datasets and an independent hospital cohort of 2,864 oncology patients, including expert contour review, supports the central claim that comprehensive data, rather than novel architectures, is what moves the field.

What carries the argument

The load-bearing mechanism is the annotation pipeline that converts a mostly unlabeled collection into a fully labeled one. Specialized region models trained on small seed sets (TotalSegmentator's 1,203 annotated volumes, Han-Seg's 42, VISCERAL, SAROS) generate whole-body pseudo-labels for the roughly 17,000 remaining scans; a shape prior built on neural implicit functions ranks every pseudo-label by its 90-percentile Hausdorff distance to a reconstruction, flagging anatomically implausible shapes for exclusion. Three complementary 'flavor' models — one trained on original ground truth, one on all pseudo-labels, one on shape-filtered pseudo-labels — are then compared per structure with statistical tests (ANOVA, Welch's ANOVA, or Kruskal-Wallis with post-hoc), and the best-performing flavor's label is selected for each of the 167 structures. A progressive merging order and targeted anatomical refinements, notably retrieval of the costovertebral joints of the ribs via a vessel-detection foundation model, complete the dataset. This pipeline is what makes the paper's scale argument possible: it turns data volume into training signal while attempting to control the noise that pseudo-labels introduce.

What would settle it

Have radiologists manually re-annotate a random sample of roughly 200 of the ~17,000 scans whose only labels came from the pseudo-labeling pipeline, covering the liver, one mid-thoracic vertebra, one rib, and buccal mucosa, and compare those manual contours against the CADS-dataset labels. If per-structure Dice on this sample is close to the model's reported performance rather than near expert-level agreement, the 'complete annotations' claim inherits the pseudo-label noise, and the model's margin over the baseline would need to be re-attributed.

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Extended reading notes

Core claim

The paper's central claim is that a data-centric strategy — systematic integration, standardization, and labeling of heterogeneous CT sources — is what lifts whole-body segmentation performance, and that established architectures are sufficient to reap the benefit. The CADS-model is trained on the assembled 22,022-volume dataset using the nnU-Net framework with region-specialized models and class-balancing oversampling for rare structures; no architectural novelty is introduced. On the 119 targets shared with TotalSegmentator, the CADS-model achieves a mean Dice of 90.52% (median 92.33%) against 88.09% (median 90.13%) for the baseline, with 71 structures improved and 44 of those improvements statistically significant. Across all 167 targets the model reports 85.87% mean Dice, with the shortfall concentrated in structures that were rarely annotated, very small (under 0.5 mL), or anatomically complex. The authors also report that training on the larger dataset improves boundary precision by an average of 3.94 mm HD95 across 18 test datasets, and that expert radiation-oncologist review deems the segmentations clinically usable for treatment planning.

Load-bearing premise

The entire annotation set rests on the assumption that models trained on small seed sets can assign accurate labels for all 167 structures across the roughly 17,000 scans that had no annotations, and that the shape-based filter that removes 'implausible' labels removes only errors, not genuine but unusual anatomy.

Editorial extensions

If this is right

  • A single CADS-model can segment 167 structures from head to knees, including 48 targets no prior whole-body model covered, so assembling separate per-organ models becomes unnecessary for this target set.
  • The documented errors in TotalSegmentator ground-truth labels (mislabeled ribs and vertebrae, organ misattributions, over-extended bladder labels) imply that models trained or evaluated on those labels inherit the errors, so existing benchmark numbers may need revisiting.
  • In the independent 2,864-patient hospital cohort, structures critical for radiation planning improve most (brainstem +43.8%, larynx +23.08%, parotid glands +19-21%), supporting direct use of the model in treatment-planning workflows.
  • The public release of the 22,022-volume dataset with 167 standardized structures gives other segmentation, landmark-detection, registration, and anatomy-guided reconstruction models a common training ground.

Reading between the lines

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

  • The paper's variance-reduction argument implies that adding even more unlabeled scans through the same pseudo-labeling loop should continue to close performance gaps on rare and small structures; a direct test would be running the pipeline on a fresh pool of, say, 20,000 hospital CTs and measuring per-structure Dice rather than the mean.
  • Because the shape-outlier filter discards rather than corrects atypical shapes, the dataset likely under-represents pathological anatomy; if that is true, per-structure performance on rare deformities (e.g., the paper's own examples of resection or transitional vertebrae) would lag well behind the reported averages.
  • The corrected rib and vertebra labels are a natural by-product resource: releasing the curated re-annotations separately would let the community re-benchmark the many models built on the previous dataset.
  • The approach treats annotation conventions — brain mask scope, trachea as air column, rectum length — as the real ceiling, so standardizing those definitions across institutions is likely to matter more than further architecture changes.
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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 introduces CADS, a data-centric framework for whole-body CT segmentation: a collection of 22,022 CT volumes from 40 sources annotated for 167 anatomical structures via iterative pseudo-labeling, shape-based quality control, and multi-flavor label selection, together with an nnU-Net-based CADS-model and a 3D Slicer plugin. The central quantitative claim is that the CADS-model outperforms the TotalSegmentator baseline on 119 mutual targets, with mean Dice 90.52% versus 88.09%, supported by evaluations across 18 public datasets and a 2,864-patient real-world oncology cohort.

Significance. If the claims hold, CADS would be a valuable community resource: the dataset is an order of magnitude larger than existing whole-body CT segmentation collections, the annotation coverage of 167 structures is substantially broader, and the data, models, and clinical plugin are publicly released. The paper is also commendably transparent about limitations, including the shape-filter exclusion of atypical cases and known imperfections in the final labels. The multi-source aggregation pipeline and the external hospital validation are serious engineering and evaluation efforts. However, the empirical support for the headline performance advantage is weakened by evaluation protocols that are partly development-involved and post-hoc curated; the dataset contribution is more robust than the specific performance comparison as currently presented.

major comments (4)
  1. [Online Methods §6.2; Results, 'Anatomical precision'] The headline comparison is computed after manually correcting 'obvious systematic errors such as mislabeled ribs and vertebrae' in the 65-image TotalSegmentator test set and excluding structures flagged as unreliable. Because §3.2 specifically engineers costovertebral joint retrieval and rib-label refinement, this curation can selectively favor CADS over the TotalSegmentator baseline. Please report results on the unmodified reference labels and on the automatically flagged subset, and make the curation protocol and exclusion list available.
  2. [Online Methods §2.4; Results, 'Cross-dataset versatility'] Flavor selection uses out-of-distribution validation from 'other labeled datasets in our collection,' explicitly including LiTS, VISCERAL, and BTCV-Abdomen, which are also among the 18 evaluation datasets. The primary cohort is described as 'heavily involved in the model development process.' This overlap means that the reported improvements on those sources are not independent of model selection. Please provide a version of the comparison restricted to sources never used in model development or flavor selection, or otherwise demonstrate that the selection procedure does not bias the reported gains.
  3. [Results, 'Anatomical precision'] The reported 95% confidence intervals for the two means overlap substantially: CADS 90.52% (88.12–92.41) versus TotalSegmentator 88.09% (85.18–90.48). As presented, the 2.4-point advantage is not statistically robust to test-set composition. Please report paired per-structure tests, effect sizes, and the number of structures with non-overlapping intervals, and clarify whether the confidence intervals are computed across structures or across scans.
  4. [Online Methods §2.1–2.3; Discussion, limitation (1)] The 167-structure 'complete annotations' rest on pseudo-labels generated by models seeded from small datasets (e.g., 42 Han-Seg volumes, 40 VISCERAL volumes, 1,203 TotalSegmentator volumes) and filtered by shape priors trained on ground truth from the same data sources. The paper acknowledges that the shape filter may exclude pathological or atypical anatomies. For the dataset claim, label correctness for rare structures such as buccal mucosa and arytenoid cartilage is not established beyond the seed sets. Please provide per-structure pseudo-label quality estimates, for example a stratified random human-review sample that includes rare structures and atypical cases, with agreement rates.
minor comments (4)
  1. [Table 1 and Figure 1 captions] There are minor typos: 'V erSe' in Table 1, 'Gluteus minius' in Figure 1 and Supplementary Figure A.2, and 'distnace' in Online Methods §2.2. These should be corrected.
  2. [Abstract and Figure 3 caption] The abstract's claim of '18 times more scans' is consistent with Table 1 (22,022 versus 1,203), but the Figure 3 caption contains a grammatical error ('This results demonstrates') that should be fixed.
  3. [Supplementary Table A.12] The structure-level comparison table is very dense and hard to read in print; consider providing it as a machine-readable CSV or splitting it into multiple smaller tables for the main text.
  4. [Results, 'From bench to bedside'] The expert review protocol is described as selecting three representative cases per structure around the median Dice score; please state how many total cases were reviewed and whether the reviewer was blinded to model identity.

Circularity Check

2 steps flagged · score 6.0 of 10

Head-to-head Dice advantage is partly constructed by flavor-selection and development-involved evaluation loops; independent hospital cohort keeps the core data-centric claim from being fully circular.

  1. fitted input called prediction [Online Methods §2.4 (Statistical ranking and selection of model flavors); Results 'Cross-dataset versatility: Dataset-level performance analysis']
    "OOD validation images come from other labeled datasets in our collection (e.g., for liver segmentation, while the model is trained on TotalSegmentator dataset (ID), its OOD validation leverages annotations from LiTS, VISCERAL, BTCV-Abdomen datasets, and etc.). We prioritize OOD performance metrics when available because they can better reflect model generalization. ... The highest-ranked flavor’s pseudo-label becomes the final label for that structure in the CADS-dataset."

    The same sources named as OOD validation (LiTS, VISCERAL, BTCV-Abdomen) are among the 18 test datasets used to report CADS-model Dice. Because the CADS-dataset label for each structure is set to the output of the flavor model that scored best on those OOD sources, the subsequently reported Dice on those sources is the label-selection objective, not an independent generalization result. The advantage over TotalSegmentator on those datasets is therefore partly forced by the construction of the training labels.

  2. other [Results, 'Cross-dataset versatility: Dataset-level performance analysis']
    "For a more nuanced evaluation, we stratify our analysis into two cohorts: (1) a primary cohort with complete ground truth annotations across all structures, which are heavily involved in the model development process and represent an optimal benchmark for accuracy assessment under ideal conditions"

    This primary cohort supplies the main mutual-target comparison (90.52% vs 88.09%, Figure 4a). Because the same data sources were used in model development (initial training seeds, shape-prior training, and OOD flavor selection), reporting the model's score on this cohort as evidence of generalization is circular: the 'test' performance is a development-set performance. The paper counters with a secondary cohort and an independent hospital cohort, so the circularity is partial.

full rationale

The paper's core contribution is a dataset and a model, and much of the derivation is an empirical engineering pipeline rather than a formal derivation. The principal circularity is in the evaluation loop: the CADS-dataset labels are assembled by choosing, per structure, the 'flavor' model with the highest Dice on OOD validation datasets drawn from the same collection, and those same datasets (LiTS, VISCERAL, BTCV-Abdomen, etc.) are then used among the 18 public test sources to report CADS-model Dice vs TotalSegmentator. This is a fitted-input-called-prediction pattern: the reported generalization score on those sources is partly the objective function used to construct the labels, not an independent prediction. A second loop is the paper's own admission that its primary evaluation cohort is 'heavily involved in the model development process,' and the headline 90.52 vs 88.09 comparison is run on this cohort; the manual correction of rib/vertebra labels in the TotalSegmentator test set (Section 6.2), after the CADS pipeline specifically engineered rib/vertebra refinements, further aligns the reference standard with CADS conventions. These loops do not make the whole paper circular: the independent 2,864-patient hospital cohort, the 48 structures with no baseline, and the released dataset/models provide externally checkable content. Self-citations to prior work on neural implicit shape priors and vesselFM are methodological and not used to rule out alternatives, so they do not add circularity. Overall: partial circularity concentrated in the headline mutual-target comparison, with independent checkpoints preventing a score above 6.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The central claim rests on the pseudo-label pipeline's ability to produce acceptable labels at scale. Hand-chosen thresholds and domain assumptions about cluster structure, shape-prior quality, registration-based labels, and expert curation are load-bearing. No new physical entities are introduced.

free parameters (6)
  • Pseudo-label exclusion threshold = 10% of lowest-scoring images per structure
    Shape-flavor model trains only on pseudo-labels above this quality rank; hand-chosen in Section 2.3 to balance quality and volume.
  • Brain coverage voxel threshold = 2,000 predicted brain voxels
    Used to filter head-related pseudo-label generation; chosen as approximately 10% of brain volume interquartile range in Section 3.1.
  • Head cropping bounding box = ±[100,100,133] voxels for brain, ±[100,100,200] for head-neck
    Postprocessing to remove hallucinations outside the head in Section 3.1; dimensions chosen by hand.
  • Costovertebral joint component size filter = 100 to 1,500 voxels
    Retains candidate joint components from vesselFM predictions in Section 3.2; threshold chosen empirically.
  • FN penalization thresholds = GT volume >90% of average means missed; GT volume <10% of average is excluded
    Defines when a missing organ is penalized in evaluation; directly affects reported Dice and HD95 values in Section 5.1.
  • NSD tolerance = 3 mm
    Set based on 1.5 mm voxel spacing in Section 5.1; used for Normalized Surface Dice computation.
assumptions (5)
  • domain assumption Cluster assumption for pseudo-labeling
    Section 2.1 states that data points of the same class form cohesive clusters in feature space; the benefit of pseudo-labeling depends on this assumption.
  • domain assumption Shape-prior quality ranking validity
    Section 2.2 assumes Hausdorff distance between a pseudo-label and its reconstruction from a shape prior trained on clean GT is a valid quality score for outlier detection.
  • domain assumption Registration-derived brain labels are accurate
    Section 2.1 uses non-rigid registration from Han-Seg CT to a SimNIBS MRI atlas to generate brain structure labels; the accuracy of these labels is assumed, not manually verified.
  • domain assumption Expert curation reliability
    Section 6.2 assumes in-house medical professionals' corrections and flags on TotalSegmentator ground truth produce a more truthful benchmark; no inter-observer agreement is reported.
  • domain assumption Variance reduction via aggregation
    Discussion invokes a central-limit-theorem-style argument that aggregating diverse pseudo-labels reduces variance errors; this heuristic is not empirically demonstrated.

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Cite this review

Pith. "Pith review of CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography." pith.science (2026). https://pith.science/paper/2AJN54YD

@misc{pith2026250722953,
  author       = {Pith},
  title        = {Pith review of: CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2AJN54YD}},
  note         = {Machine review of arXiv:2507.22953}
}
read the original abstract

Accurate delineation of anatomical structures in volumetric CT scans is crucial for diagnosis and treatment planning. While AI has advanced automated segmentation, current approaches typically target individual structures, creating a fragmented landscape of incompatible models with varying performance and disparate evaluation protocols. Foundational segmentation models address these limitations by providing a holistic anatomical view through a single model. Yet, robust clinical deployment demands comprehensive training data, which is lacking in existing whole-body approaches, both in terms of data heterogeneity and, more importantly, anatomical coverage. In this work, rather than pursuing incremental optimizations in model architecture, we present CADS, an open-source framework that prioritizes the systematic integration, standardization, and labeling of heterogeneous data sources for whole-body CT segmentation. At its core is a large-scale dataset of 22,022 CT volumes with complete annotations for 167 anatomical structures, representing a significant advancement in both scale and coverage, with 18 times more scans than existing collections and 60% more distinct anatomical targets. Building on this diverse dataset, we develop the CADS-model using established architectures for accessible and automated full-body CT segmentation. Through comprehensive evaluation across 18 public datasets and an independent real-world hospital cohort, we demonstrate advantages over SoTA approaches. Notably, thorough testing of the model's performance in segmentation tasks from radiation oncology validates its direct utility for clinical interventions. By making our large-scale dataset, our segmentation models, and our clinical software tool publicly available, we aim to advance robust AI solutions in radiology and make comprehensive anatomical analysis accessible to clinicians and researchers alike.

Figures

Figures reproduced from arXiv: 2507.22953 by the authors.

Figure 1
Figure 1. Overview of CADS framework’s anatomical coverage. Comprehensive visualization of 167 anatomical targets segmented by the CADS framework, spanning from head to knees. These clinically relevant structures are organized into nine anatomical groups: (1) major abdominal organs, primary tho￾racic organs (lungs), and major abdominal vasculature; (2) complete set of individual vertebrae from cervical to lumbar regions; (3) … view at source ↗
Figure 2
Figure 2. Overview of the multi-stage development process for whole-body CT segmentation CADS-model. The pipeline consists of four key stages: (1) initial region-specific model training and pseudo-label generation across 22,022 CT volumes, utilizing specialized models for nine anatomical regions covering all 167 target structures; (2) automated quality control employing neural implicit functions and shape priors to filter unr… view at source ↗
Figure 3
Figure 3. Structure-level performance analysis. Visualization of segmentation performance across 167 anatomical structures grouped by anatomical systems. The radar plot presents Dice scores (increasing radially from 0 at center to 1 at periphery) for the CADS-model compared against existing approaches, evaluated on diverse validation sets from 18 public datasets. Performance is shown for CADS-model (orange), TotalSegmentator … view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Clinical evaluation of CADS-model performance. Multi-faceted analysis of segmentation performance across different validation scenarios: (a) Comparison between CADS-model and baseline model TotalSegmentator on mutual anatomical targets, evaluated across primary (comple…
Figure 5
Figure 5. Figure 5: Qualitative assessment of CADS-model segmentation performance. Visual evaluation of segmentation results across three key aspects: (a) Comparison of skeletal structure segmentation between existing methods and CADS-model, demonstrating significant improvements aligned …

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.