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REVIEW 3 major objections 6 minor 41 references

landmarker: a Toolkit for Anatomical Landmark Localization in 2D/3D Images

T0 review · 3 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This paper presents landmarker, an extensible Python toolkit for anatomical landmark localization in 2D/3D medical images, and reports that models built with it outperform published baselines on pelvis X-rays and skull CT scans.

desk verdict Useful, open-source toolkit for medical landmark localization; the accuracy claims need a protocol-equivalence check before taking the gains at face value. read the letter →

arxiv 2501.10098 v2 pith:2MBAXC7J submitted 2025-01-17 cs.CV cs.AIcs.LG

Jef Jonkers , Luc Duchateau , Glenn Van Wallendael , Sofie Van Hoecke This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords anatomicallandmarklocalizationkeypointdetectionmedicalimageanalysisheatmapregressionadaptiveone-hotensemblePyTorchtoolkit2D/3Dimaging
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

landmarker is a Python software package that standardizes the full pipeline for anatomical landmark localization in 2D and 3D medical images, from loading medical image formats through heatmap generation, model training, decoding, and evaluation. The paper's central claim is that this toolkit is more complete and more modular than general-purpose pose-estimation packages for medical use, and that models implemented within it match or exceed published accuracy. Concrete evidence: an adaptive-heatmap spatial configuration network reaches a mean point error of 1.61 mm on OAI pelvis X-rays versus 3.14 mm reported for an attention U-Net, and a one-hot ensemble reaches 1.39 mm on the MML 3D skull test set versus 1.96 mm for Pruning-ResUNet3D. If correct, researchers can build, reproduce, and compare landmark-localization methods with less engineering overhead while retaining state-of-the-art accuracy.

What carries the argument

The central machinery is the modular pipeline connecting four flexible pieces: dataset classes that ingest medical formats and keep landmark coordinates aligned under affine transforms; a heatmap generator whose distribution parameters (e.g., Gaussian sigmas and rotation) can be fixed, scheduled, or learned; a decoder that turns predicted heatmaps into coordinates via argmax, weighted spatial mean, soft-argmax, or local weighted spatial mean; and interchangeable models such as the spatial configuration network and a one-hot ensemble. Adaptive heatmap regression carries the 2D benchmark: letting the Gaussian covariance be learned during training lets a standard spatial configuration network outperform a baseline that uses fixed heatmaps. The one-hot ensemble carries the 3D benchmark by framing landmark localization as per-pixel classification and combining multiple predictions.

What would settle it

Re-train the two baselines (U-Net with Attention on the OAI pelvis X-rays, Pruning-ResUNet3D on the MML 3D skull data) inside the landmarker pipeline with the same folds and preprocessing; if they achieve point errors close to landmarker's 1.61 mm and 1.39 mm respectively, the outperformance claim collapses to a pipeline artifact.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that a single extensible toolkit can cover the methodological diversity of anatomical landmark localization—static, adaptive, and one-hot heatmap regression—without sacrificing accuracy. The paper demonstrates this with two benchmarks: on the Osteoarthritis Initiative pelvis X-ray dataset, a spatial configuration network with learnable Gaussian heatmap parameters implemented in landmarker achieves PE 1.61 mm and SDR up to 95.85% at 4 mm, compared with PE 3.14 mm for the U-Net with Attention reported in [Pei et al., 2023]; on the MML 3D skull dataset, a one-hot ensemble achieves test PE 1.39 mm and SDR 96.31% at 4 mm, compared with 1.96 mm for Pruning-ResUNet3D in [He et al., 2024]. The implied claim is that the package's modular abstractions—datasets, heatmap generators, decoders, models, losses, metrics—make it possible to implement these leading methods quickly and to benchmark them fairly on medical formats.

Load-bearing premise

The load-bearing premise is that the baseline numbers quoted from [Pei et al., 2023] and [He et al., 2024] were produced with equivalent data splits, preprocessing, and evaluation protocol, so the reported accuracy gaps reflect the models rather than hidden pipeline differences.

Editorial extensions

If this is right

  • A research group can reproduce the reported pelvis and skull results using only the public package and dataset imports, without reimplementing heatmap generation or decoding.
  • New landmark localization methods can be dropped into the same training and evaluation harness, making comparisons across papers more apples-to-apples than ad hoc code.
  • Because the toolkit handles 3D volumes directly rather than reducing to slices, methods developed in it transfer to volumetric medical tasks such as skull and orthopedic landmarking.
  • Adaptive heatmap models implemented here could serve as stronger baselines in future medical imaging papers than the fixed-heatmap baselines currently used.

Reading between the lines

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

  • If the benchmark gaps hold under identical cross-validation folds, they suggest that learnable heatmap covariance and one-hot ensembles are worth adopting as default components in medical landmark pipelines, not just package features.
  • The same modular separation of heatmap generator, decoder, and model could be applied to other dense prediction tasks, such as control-point detection for registration or counting, by reusing the evaluation and visualization modules.
  • A straightforward testable extension is to add uncertainty-aware decoding to the toolkit, which the paper names as future work, letting the reported accuracy gains be paired with confidence intervals on each landmark.
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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

3 major / 6 minor

Summary. The paper presents landmarker, a PyTorch-based Python package for anatomical landmark localization in 2D/3D medical images. It describes the modular architecture (data handling, heatmap generation/decoding, models/losses, evaluation/visualization) and provides code listings for using the package. The authors report two benchmark experiments: an SCN with adaptive heatmaps on pelvis X-rays (PE 1.61 mm vs 3.14 mm for a U-Net with Attention) and a one-hot ensemble on a 3D skull CT dataset (test PE 1.39 mm vs 1.96 mm for Pruning-ResUNet3D). The paper also frames the toolkit as addressing gaps in general-purpose pose-estimation libraries for medical imaging.

Significance. If the toolkit is as functional as the code listings and public repository suggest, it provides a valuable, modular alternative to existing pose-estimation frameworks for medical landmark tasks, with support for NIfTI/DICOM, 2D/3D data, and multiple heatmap regression paradigms. The reproducibility evidence (public code, example listings) is a definite strength. However, the accuracy-enhancement claim rests on two benchmark comparisons whose protocol equivalence to the cited baselines is not established; if that is resolved, the paper would be a useful contribution to the medical imaging software ecosystem.

major comments (3)
  1. [Section 4.1, Table 2] The claim that the landmarker SCN with adaptive heatmaps outperforms the U-Net with Attention (PE 1.61 vs 3.14 mm) is not independently verifiable without protocol-equivalence details. The text only states that 'a 5-fold validation approach, as suggested by Pei et al., obtains the results'; it does not state whether the same fold assignments, image resolution, preprocessing (e.g., resampling, contrast normalization), or evaluation code were used for both methods, nor whether the baseline numbers were copied from the original paper. Please provide the exact protocol, or re-run the baseline in the landmarker pipeline, and report variance (e.g., per-fold ranges or standard deviations).
  2. [Section 4.1, Table 3] The comparison against Pruning-ResUNet3D on the MML skull dataset has the same protocol-equivalence problem. The text says the dataset is 'the same as the data subset, only with complete landmarks, used as a benchmark in He et al.' but does not describe how the train/validation/test split, landmark definitions, or evaluation metric were matched. In addition, the method compared is a one-hot ensemble from a same-group preprint (Jonkers et al., 2025); since the preprint is not yet published, the method details and hyperparameters are not available in the cited literature. Please specify the split and evaluation protocol in detail, provide hyperparameters or a reference to a versioned repository, and ideally include error bars from multiple runs.
  3. [Abstract and Section 4.1] The broad statement in the Abstract that landmarker 'enhances the accuracy of landmark identification' is supported only by the two benchmark tables. Given the protocol-equivalence concerns above, this claim is stronger than the current evidence. Consider qualifying it (e.g., 'in the experiments presented here') or adding the missing protocol details. This is not a request to remove the benchmark, but to align the claim with the evidence.
minor comments (6)
  1. [Figure 2 caption] The caption contains a typo: 'pacakge' should be 'package'.
  2. [Listing 4 caption] The caption says 'Loading data into a LandmarkDataset' but the code calls inspection_plot; the caption should describe visual inspection instead.
  3. [Figure 4 caption] The caption lists '(a) Endoscopic images, (b) ISBI2015, and (c) Pelvis X-rays' but the subplot labels in the figure are '(a) ISBI2015 dataset', '(b) Pelvis X-rays', '(c) Endoscopic images'; the ordering is inconsistent.
  4. [Table 2 header] The column header '4 mmU-Net w/ Attention' is missing a space; it should be '4 mm U-Net w/ Attention'.
  5. [Title and running head] The running head contains 'I MAGES' with an incorrect space; it should read 'IMAGES'.
  6. [Listing 5] For reproducibility, the listing could also state the image resolution, batch size (currently 1), and hardware used for training; the current listing shows only the optimizer settings.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: benchmark numbers are empirical evaluations, not derived predictions from fitted constants or self-citation chains.

full rationale

The paper is a software description, not a derivation chain. Its central claims are functionality claims about the landmarker package, and the benchmark results in Tables 2 and 3 are empirical evaluations of trained models on held-out data (OAI pelvis X-rays and MML 3D skull CTs). There is no equation in which an output is defined in terms of an input, no fitted parameter renamed as a prediction, and no uniqueness theorem imported from prior work to force a choice. The one self-citation, [Jonkers et al., 2025] for the one-hot ensemble method, is a method reference; the reported PE/SDR values are presented as results obtained with that method implemented in landmarker, and they are falsifiable against the public code and external data. The comparison to published baselines rests on an assumption of matching data splits and preprocessing, which is a protocol-equivalence concern about correctness and reproducibility, not a circular-reasoning defect. Thus the paper is self-contained against external benchmarks in the sense relevant to the circularity analysis.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The central claim is a software functionality claim, so the ledger records the hyperparameters that affect benchmark results and the comparability assumptions behind the benchmark tables. No new physical entities are introduced.

free parameters (3)
  • Gaussian heatmap sigma (initial value, learnable) = 3 (initial)
    Listing 3 sets sigmas=3 and allows the covariance to be learnable; this hand-chosen initialization affects heatmap targets and downstream benchmark outcomes.
  • GaussianHeatmapL2Loss alpha = 5
    Listing 5 sets alpha=5 in the loss function, a hand-chosen weighting hyperparameter used in the example training loop.
  • Training hyperparameters (LR, momentum, weight decay, epochs) = lr=1e-6, momentum=0.99, weight_decay=1e-3, epochs=100
    Listing 5 reports these choices for the ISBI example; the OAI and MML benchmark sections do not report the exact hyperparameters used, leaving a gap in reproducibility.
assumptions (3)
  • standard math PyTorch and MONAI provide numerically correct primitives for the implemented operations.
    The package delegates tensor operations, resampling, and I/O to these libraries, so the toolkit's correctness assumes the libraries behave as documented.
  • domain assumption The cited benchmark protocols and baseline numbers are directly comparable to the landmarker runs.
    Section 4.1 compares against numbers from Pei et al. 2023 and He et al. 2024 without re-running the baselines in the same framework.
  • domain assumption The benchmark datasets are used with the same splits and landmark definitions as in the cited works.
    Section 4.1 states a 5-fold approach 'as suggested by Pei et al.' and a subset 'used as a benchmark in He et al.', but exact preprocessing and landmark definitions are not fully specified.

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Cite this review

Pith. "Pith review of landmarker: a Toolkit for Anatomical Landmark Localization in 2D/3D Images." pith.science (2026). https://pith.science/paper/2MBAXC7J

@misc{pith2026250110098,
  author       = {Pith},
  title        = {Pith review of: landmarker: a Toolkit for Anatomical Landmark Localization in 2D/3D Images},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2MBAXC7J}},
  note         = {Machine review of arXiv:2501.10098}
}
read the original abstract

Anatomical landmark localization in 2D/3D images is a critical task in medical imaging. Although many general-purpose tools exist for landmark localization in classical computer vision tasks, such as pose estimation, they lack the specialized features and modularity necessary for anatomical landmark localization applications in the medical domain. Therefore, we introduce landmarker, a Python package built on PyTorch. The package provides a comprehensive, flexible toolkit for developing and evaluating landmark localization algorithms, supporting a range of methodologies, including static and adaptive heatmap regression. landmarker enhances the accuracy of landmark identification, streamlines research and development processes, and supports various image formats and preprocessing pipelines. Its modular design allows users to customize and extend the toolkit for specific datasets and applications, accelerating innovation in medical imaging. landmarker addresses a critical need for precision and customization in landmark localization tasks not adequately met by existing general-purpose pose estimation tools.

Figures

Figures reproduced from arXiv: 2501.10098 by the authors.

Figure 1
Figure 1. Taxonomy of (deep learning) landmark local [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Flowchart of the different sub-packages of the [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Example of results of running the inspection_plot on the ISBI2015 dataset. 1 from landmarker . dataset import get_cepha_landmark_datasets 2 3 data_dir = ... 4 ds_train , ds_test1 , ds_test2 = get_cepha_landmark_datasets ( data_dir ) Listing 2: Loading data into a LandmarkDataset through the dataset module. Once the data is loaded in the proper format, we can set up the heatmap generator to generate a heatmap from la… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Examples of landmark predictions using the [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.