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Automated Thoracolumbar Stump Rib Detection and Analysis in a Large CT Cohort

T0 review · 5 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Thoracolumbar stump ribs can be detected, measured, and classified automatically from CT scans, using the first roughly 22 mm of rib direction and position to reach F1 0.84 even on partially visible ribs.

desk verdict Solid segmentation and measurement pipeline with a new stump-rib classification task, but the morphological classifier's labels come from the length threshold it claims to go beyond, and Appendix A.2 undercuts the 'beyond length' interpretation. read the letter →

arxiv 2505.05004 v1 pith:TJQ44WLQ submitted 2025-05-08 cs.CV cs.LG

Hendrik Möller , Hanna Schön , Alina Dima , Benjamin Keinert-Weth , Robert Graf , Matan Atad , Johannes Paetzold , Friederike Jungmann
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Rickmer Braren Florian Kofler Bjoern Menze Daniel Rueckert Jan S. Kirschke
This is my paper · ORCID
classification cs.CVcs.LG
keywords thoracolumbarstumpribsegmentationcomputedtomographydeeplearninglengthmeasurementmorphologicalfeaturestransitionalvertebraesupportvectormachine
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 aims to make the detection of thoracolumbar stump ribs—the short, malformed ribs at the bottom of the thoracic spine that mark transitional vertebrae or numbering anomalies—fully automatic in CT scans. It trains a deep-learning segmentation model that captures the whole rib, including the part near the spine that public tools miss, then measures each rib with an iterative path-following algorithm, reporting that 98.2% of lowest ribs are measured correctly. The authors show that stump ribs are not just shorter; their first few centimeters point more downward, sideways, and posteriorly, and the rib is proportionally thinner. A simple support-vector machine using these early-direction and start-position features separates stump ribs from regular ribs with an average F1 of 0.84 using only the first roughly 22 mm of a rib. If this is right, radiologists could flag transitional vertebrae and spine-numbering anomalies in ordinary or narrow-field CT scans without manual rib tracing.

What carries the argument

The load-bearing mechanism is a three-stage pipeline: a high-resolution deep-learning segmentation model trained at 0.8 mm isotropic resolution on 55 manually corrected CT volumes, which recovers the rib head near the vertebra that public segmentation tools omit; the rib-length measurement algorithm (RLMA), an iterative path walk that averages candidate points within a 14.5 to 15.5 mm circular shell of the current path point, projects each step back onto the segmentation surface, and sums the path; and morphological features derived from that path—the posterior distance between the rib start and the vertebra corpus center (PDRC) and the first n direction vectors between consecutive path points (n-PPR)—which are fed to a support-vector machine. The conceptual move is using only the first four path points, about 22 mm, plus the start-vertebra relation to classify a stump rib without needing the full rib length.

What would settle it

Take 100 CT scans with fully visible lowest ribs, have two independent radiologists measure the same ribs with a standardized curved reformatting protocol, and compare those measurements to the algorithm's lengths; if the automated lengths disagree systematically, especially within 10 mm of the 38 mm threshold, the ground-truth labels and the F1 0.84 result are called into question.

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Extended reading notes

Core claim

The paper claims that thoracolumbar stump ribs—ribs of 38 mm or less at the lowest thoracic level—are automatically detectable from CT, and that their distinguishing signal is not only length. Starting from the vertebra corpus, the first roughly 22 mm of a stump rib is angled more downward, sideways, and posteriorly than a full rib, and the rib has a lower volume-to-length ratio. The paper's segmentation model produces dense whole-rib masks (Dice 0.997 on its test set versus 0.751 to 0.779 for public baselines), an iterative path-point algorithm measures rib length with 98.2% success, and a linear support-vector machine on the first four path-point directions plus the rib-start/vertebra-center distance separates stump ribs from regular ribs with an average F1 of 0.84 using only the initial 22 mm of the rib. This implies stump ribs can be flagged even when the scan's field of view cuts the rib short.

Load-bearing premise

The ground-truth labels for stump ribs come from the paper's own automated length measurement crossing a fixed 38 mm cutoff, with no independent or clinically validated rib-length standard; if those length estimates are systematically biased by segmentation errors, the reported accuracy may partly reflect the error pattern rather than true anatomy.

Editorial extensions

If this is right

  • Stump ribs can be discovered automatically in routine CT, reducing manual measurement and helping with vertebral numbering and detection of transitional anomalies.
  • Because classification needs only the first 22 mm of a rib, limited field-of-view CT scans—where whole ribs are not visible—become usable for stump-rib assessment.
  • The measured morphological features offer quantitative descriptors of stump-rib shape that could supplement the length-only definition in clinical reporting.
  • The released model weights and masks let other groups produce whole-rib segmentations and length measurements on their own CT data without retraining.
  • Accurate whole-rib segmentation with high Dice and low surface distance supports downstream biomechanical or surgical-planning analyses of the thoracolumbar junction.

Reading between the lines

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

  • Beyond the paper: because the authors' own threshold-shift experiment shows no peak at 38 mm but a small peak near 41 mm, the fixed length cutoff may not carve anatomy at a natural boundary; a population study relating rib length to vertebral level could test whether the cutoff should be revised.
  • Beyond the paper: the same 22 mm directional signature might help identify transitional vertebrae in MRI or low-dose scans, where whole-rib segmentation is unreliable—this is untested here.
  • Beyond the paper: since the authors note that some non-stump ribs with large posterior start distances had enlarged foveae or corpus anomalies, adding vertebra morphometry to the feature set could further separate the groups, a hypothesis they leave for future work.
  • Beyond the paper: a pre-registered test on whole-body CT with ribs truncated at varying levels could establish how robust the F1 0.84 is when the visible rib length varies continuously, and whether morphological classification can be embedded in automated spine-labeling pipelines.
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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 proposes a complete pipeline for automated thoracolumbar stump rib detection in CT. The authors train a high-resolution nnU-Net for whole-rib segmentation using a mix of private in-house and public data, introduce an iterative rib-length measurement algorithm (RLMA), extract morphological features (DRC, n-PPR, volume-to-length ratio), and train SVMs to classify stump ribs from regular ribs using only the first roughly 22 mm of rib. On 45 manually corrected test subjects the segmentation model achieves Dice 0.997 versus 0.751--0.779 for public baselines, the RLMA is reported to achieve a 98.2% success rate on expert review, and a linear SVM with 4-PPR and DRC reaches an average F1 of 0.84. Code, model weights, and segmentation masks are publicly released.

Significance. If the results hold, the segmentation model, length-measurement algorithm, and released masks are valuable public resources for rib analysis in CT, and the partial-field-of-view morphological classifier would be clinically useful for surgical planning and vertebral enumeration. The segmentation comparisons on manually corrected test data are convincing and reproducible, and the authors are transparent about releasing code and data. However, the central classification claim currently depends on a self-referential length threshold rather than an independent anatomical gold standard, and on selecting the best SVM configuration from many alternatives on the same test data. The claimed morphological signal 'beyond the length' is not yet established, so the overall significance is conditional on additional validation or a more modest reframing.

major comments (5)
  1. [Section 2.4, Section 2.6, Table 5] The SR/non-SR labels used to train and evaluate the SVM are obtained by comparing the RLMA-computed rib length with the 38 mm threshold from Wigh [3], and the Likert review in Section 2.6 only checks that segmentation errors did not alter the computed length. There is no independent anatomical rib-length standard. Therefore the reported F1=0.84 measures agreement between morphological features and the RLMA length threshold, not agreement with a clinical ground truth. The paper should obtain an independent length or anatomy reference on at least a subset, or explicitly reframe the classifier as a length-proxy model for the 38 mm definition.
  2. [Appendix A.2, Figure A.9] The threshold-shift experiment shows no peak in SVM F1 at the 38 mm threshold and a smooth decay with increasing threshold, which the authors themselves interpret as evidence that either length-based labeling is suboptimal or that the features smoothly track rib length. This directly contradicts the abstract's claim that the morphological features are 'beyond the length' and classify stump ribs as a discrete entity. This load-bearing evidence requires either independent anatomical validation of a discrete stump-rib class or a revised conclusion presenting the method as a length-proxy classifier for partial-field-of-view scans.
  3. [Section 2.6, Table 5] The best configuration (4-PPR+DRC, linear kernel) is selected from a grid of 14 feature/kernel combinations and evaluated on the same 70/30 subject-wise splits across 10 seeds. Selecting the maximum F1 over this grid biases the reported 0.84 upward, and no nested resampling or multiple-comparison correction is described. The authors should report selection-corrected estimates, use a separate validation split for model selection, or pre-specify the configuration.
  4. [Table 4, Section 3.2] The 98.2% 'correct measure' rate is obtained by summing ratings (5), (4), and (3), but rating (3) explicitly indicates that the rib is cut off at the image border and the measured length is that of the visible section only. For such ribs, the full rib length is unknown unless the visible length already exceeds 38 mm, so counting all rating-(3) cases as correct full-length measurements inflates the success rate. The authors should separate truly complete measurements from truncated ones and report the rate of valid stump-rib labeling accordingly.
  5. [Section 2.8, Section 3.3] The Wilcoxon rank-sum tests compare 2464 ribs from 648 subjects as if every rib were an independent observation, despite ribs being clustered within subjects. Ignoring within-subject correlation can inflate significance. The reported p-values are very small and the group differences are large, so the qualitative conclusions would probably survive a corrected analysis, but the authors should repeat the feature comparisons with subject-level aggregation or a mixed-effects model to confirm.
minor comments (5)
  1. [Section 2.8] The heading 'Statisticial Analysis' contains a typo and should read 'Statistical Analysis'.
  2. [Equation 2] The definition of ASSD divides by |X|+|Y|, but X and Y are the full rib volumes while A and B are the boundary surfaces; the standard average symmetric surface distance should divide by |A|+|B|, the total number of boundary points. Please correct the formula or clarify the notation.
  3. [Table 3] Table 3 reports evaluation on 20 manually corrected test subjects from RibFrac, while Section 2.2 describes 45 randomly chosen test subjects from VerSe and RibFrac combined. Please clarify how the 20-subject RibFrac subset relates to the 45-subject test set.
  4. [Figure 6] The scatter plot in Figure 6 shows substantial overlap between the two classes, while the text describes a 'clear trend'; consider adding marginal distributions or a decision boundary to make the group separation more visible.
  5. [Section 3.1] The name 'TotalSegmentor' is misspelled in the text and should be 'TotalSegmentator'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the SR label comes from an external 38 mm length threshold, and the morphological SVM features are not algebraic functions of the length.

full rationale

The derivation chain is self-contained. Stump-rib labels are defined by an externally cited length threshold (Wigh's <=38 mm) applied to the RLMA-computed rib length, while the morphological features used for classification (DRC and n-PPR) are measured from the same rib segmentation but are not algebraic transforms of the length value. The SVM is therefore trained to predict an externally thresholded label from independent geometric features, not to re-state a fitted input. The appendix's observation that F1 varies smoothly with the threshold and has no peak at 38 mm is an honest validity caveat about whether the length threshold captures a discrete anatomical category, but it does not make the classification equivalent to its input by construction. The two overlapping-author tools cited (Btrfly Net for vertebra instance segmentation and panoptica for evaluation metrics) are auxiliary, externally published components and do not define the outcome or carry the central claim. Model selection across kernels and feature sets on the same data is a potential overfitting concern, which is a correctness risk rather than a circularity issue. No equation in the paper reduces a predicted quantity to a fitted parameter, and no load-bearing argument rests solely on a self-citation.

Assumptions & free parameters 4 free parameters · 6 assumptions · 0 invented entities

The central result depends on a small number of chosen numeric settings (RLMA search radius, SVM kernel selection, resampling resolutions) and on domain assumptions about anatomy, the 38 mm definition, and the reliability of expert review. No new physical entities are postulated. The largest burden is that ground-truth stump-rib labels are the 38 mm threshold applied to the authors' own automated length measurement.

free parameters (4)
  • RLMA candidate search radius = 14.5 to 15.5 mm
    Chosen by hand in Section 2.4 to define candidate path points around the previous path point. No sensitivity analysis is given, and this radius directly affects the computed rib length.
  • SVM kernel and feature configuration = polynomial degree 5 or linear kernel, selected by F1
    Section 2.6 and Table 5 report 14 configurations over 10 seeds with no separate held-out model-selection split. The best F1 of 0.84 is chosen from this search, making the number potentially optimistic.
  • Segmentation resampling resolution and patch size = 0.8 mm isotropic, 192x192x192 patch
    Section 2.2: these settings affect whether the proximal rib head is captured and therefore affect length measurements. They are standard training hyperparameters rather than fitted scientific constants.
  • RLMA mask resampling resolution = 0.5 mm isotropic
    Section 2.4: cropped rib masks are rescaled to 0.5 mm resolution; this discretization has a small but unquantified effect on summed path length.
assumptions (6)
  • domain assumption Stump ribs are defined as ribs with length <= 38 mm and occur only at the lowest thoracic level.
    Taken from Wigh [3] in Section 1 and used in Sections 2.4 and 3.3 to form ground-truth labels for the classifier.
  • domain assumption Correct rib annotation is the filled bone outline up to, but not including, the costal cartilage and sternum.
    Defines the segmentation target in Section 2.2 and therefore determines what the RLMA length actually measures.
  • domain assumption Each vertebra can have at most two ribs and each rib can be assigned to only one vertebra.
    Instance assignment constraint in Section 2.3; failures here would corrupt per-rib features and length calculations.
  • domain assumption Expert Likert review is an adequate standard for rib length correctness.
    Section 2.6 and Limitations 3.4 state there is no gold standard for manual rib length; the 98.2% success rate is based entirely on this review.
  • domain assumption Excluding ribs whose last rib is not visible, with missing vertebra masks, or with imaging artifacts does not bias the stump-rib analysis.
    Section 2.6 and 3.3 apply these exclusions; the authors assert no observed bias but provide no quantitative comparison of excluded versus included cases.
  • domain assumption The trained nnUNet model transfers from 55 training subjects to the public datasets.
    Section 2.2: all downstream length and morphology measurements depend entirely on these segmentations generalizing to new CT scans.

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Cite this review

Pith. "Pith review of Automated Thoracolumbar Stump Rib Detection and Analysis in a Large CT Cohort." pith.science (2026). https://pith.science/paper/TJQ44WLQ

@misc{pith2026250505004,
  author       = {Pith},
  title        = {Pith review of: Automated Thoracolumbar Stump Rib Detection and Analysis in a Large CT Cohort},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TJQ44WLQ}},
  note         = {Machine review of arXiv:2505.05004}
}
read the original abstract

Thoracolumbar stump ribs are one of the essential indicators of thoracolumbar transitional vertebrae or enumeration anomalies. While some studies manually assess these anomalies and describe the ribs qualitatively, this study aims to automate thoracolumbar stump rib detection and analyze their morphology quantitatively. To this end, we train a high-resolution deep-learning model for rib segmentation and show significant improvements compared to existing models (Dice score 0.997 vs. 0.779, p-value < 0.01). In addition, we use an iterative algorithm and piece-wise linear interpolation to assess the length of the ribs, showing a success rate of 98.2%. When analyzing morphological features, we show that stump ribs articulate more posteriorly at the vertebrae (-19.2 +- 3.8 vs -13.8 +- 2.5, p-value < 0.01), are thinner (260.6 +- 103.4 vs. 563.6 +- 127.1, p-value < 0.01), and are oriented more downwards and sideways within the first centimeters in contrast to full-length ribs. We show that with partially visible ribs, these features can achieve an F1-score of 0.84 in differentiating stump ribs from regular ones. We publish the model weights and masks for public use.

Figures

Figures reproduced from arXiv: 2505.05004 by the authors.

Figure 1
Figure 1. The flow of our three utilized datasets. We used the private in-house dataset for [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Two example subjects (rows) of the RibFrac dataset. We compare the original [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. An example showing our transition from semantic rib segmentation to instance [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: 2D Schematic illustration of our 3D rib length measurement algorithm (RLMA). [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Example 2D image to showcase some of our calculated 3D features. Blue is the [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: Analyzed ribs classes are colored as follows: SR (orange) and regular rib (blue). [PITH_FULL_IMAGE:figures/full_fig_p015_6.png]
Figure 7
Figure 7. Figure 7: Our analyzed rib classes are colored as follows: SR (orange) and regular rib [PITH_FULL_IMAGE:figures/full_fig_p016_7.png]

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Forward citations

Cited by 1 Pith paper

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