Markerless Augmented Reality Registration for Surgical Guidance: A Multi-Anatomy Clinical Accuracy Study

Andrey Finegersh; Brian Hargreaves; Bruce Daniel; Christoph Leuze; Fabian Necker; Fred M Baik; Jake Lee; Jie Ying Wu; Michelle Chen; Vasu Divi

REVIEW 3 major objections 5 minor 26 references

Markerless Augmented Reality Registration for Surgical Guidance: A Multi-Anatomy Clinical Accuracy Study

T0 review · 3 major / 5 minor · reviewed 2026-08-04 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A depth-only, markerless AR pipeline registered CT models to live surgery with 3–4 mm median error across feet, ear, and lower leg, without fiducials.

desk verdict Live multi-anatomy markerless AR data is a real step forward, but the headline accuracy number rests on a self-referential measurement; treat the 3–4 mm as provisional until an independent absolute reference is used. read the letter →

arxiv 2511.02086 v2 pith:JZ5ZLBLP submitted 2025-11-03 cs.CV

Yue Yang , Fabian Necker , Christoph Leuze , Michelle Chen , Andrey Finegersh , Jake Lee , Vasu Divi , Bruce Daniel

show 3 more authors

Brian Hargreaves Jie Ying Wu Fred M Baik

This is my paper · ORCID

classification cs.CV

keywords markerlessaugmentedrealitysurgicalguidanceHoloLens2depth-basedregistrationpointcloudclinicalaccuracystudysurfacetracingfiducial-free

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

The paper demonstrates that a markerless, depth-only augmented reality registration system on a head-mounted display can align preoperative CT skin models to small or low-curvature anatomies during real surgery, achieving a pooled median per-point error of 3.9 mm. This approaches the roughly 5 mm error threshold considered acceptable for moderate-risk surgical tasks, while eliminating the workflow burden of fiducial markers. The authors validate their surface-tracing error measurement against CT ground truth in preclinical tests, then report clinical results across seven intraoperative trials on feet, ear, and lower leg. If accurate, this would be a practical step toward routine markerless AR guidance in surgery.

What carries the argument

The registration pipeline's load-bearing components are: (1) region-specific depth-bias correction using a tracked stylus to solve an orthogonal Procrustes fit between ground-truth surface samples and AHAT depth points; (2) human-in-the-loop ROI initialization, where the user roughly aligns a translucent virtual model to the target anatomy to crop the scene and bound the search region; (3) robust coarse alignment using FPFH features and TEASER++ to handle outliers and large misalignments; and (4) fine point-to-plane ICP with decreasing residual thresholds. The surface-tracing error metric, validated against CT, provides the clinical accuracy numbers.

What would settle it

Simultaneously measure the true position of the virtual overlay using an external optical tracker (independent of the HoloLens depth sensor) and compare it to the skin trace used for error evaluation; if the two disagree by substantially more than the reported median error, the claimed ~3–4 mm accuracy is an artifact of common-mode stylus error.

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Extended reading notes

Core claim

The central claim is that a depth-only registration pipeline, combining a brief human-in-the-loop initialization with global (FPFH/TEASER++) and local (point-to-plane ICP) alignment, can achieve clinically relevant accuracy on anatomies that are small or have low curvature. In live surgical settings, the pooled per-point median error was 3.9 mm, with anatomy-specific medians of 3.2 mm (feet), 4.3 mm (ear), and 5.3 mm (lower leg), and 5 mm surface coverage ranging from 72–95%. Preclinical validation showed that stylus-based AR surface tracing reproduces CT-derived skin-to-bone distances with a median discrepancy of about 0.8 mm, supporting the use of this tracing method as an intraoperative a

Load-bearing premise

The measured accuracy assumes the HoloLens-tracked stylus is an independent ground truth, but the same stylus is used to calibrate the depth-bias correction, so any systematic stylus error would be invisible in the reported overlay-to-skin distances.

Editorial extensions

If this is right

Markerless, fiducial-free AR guidance could meet clinical accuracy thresholds for moderate-risk tasks on small anatomies, reducing OR setup time and invasiveness.
The human-in-the-loop initialization may be generalized to other anatomies where fully automatic global registration is unreliable due to symmetry or low curvature.
The validated surface-tracing metric offers a practical, CT-referenced method for evaluating AR overlay accuracy in situ on patients.
The reported anatomy-specific differences (feet more accurate than leg) suggest that exposure and surface curvature are key factors to optimize in future markerless systems.
If reproduced on more patients, the approach could support visualizing internal structures (fibula, foot bones) directly over the skin without external tracking hardware.

Reading between the lines

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

The reported accuracy may understate true overlay displacement if the HoloLens-tracked stylus carries systematic error, since the same stylus is used both for depth-bias correction and for the error measurement; an independent optical tracker would settle this.
The method's dependence on a brief user alignment step means its clinical reliability partly rests on operator skill; automating this step could make the system more robust but may reduce accuracy on symmetric anatomies.
The success on skin surfaces suggests the pipeline might extend to soft-tissue deformation tracking if combined with non-rigid registration, though the current rigid assumption limits that application.
The between-anatomy error differences imply that surfaces with richer curvature (feet) are easier to align, so future work could focus on feature-poor regions like the lower leg with additional constraints (e.g., limb axis priors).

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Adam, quick read on arXiv:2511.02086.

The genuinely new thing here is the data: seven intraoperative trials (feet, ear, lower leg) on a depth-only HoloLens 2 pipeline, with 500+ traced points per trial. No one else has published live-patient accuracy numbers for this kind of fiducial-free, depth-only AR registration. The authors also did a sensible preclinical sanity check: tracing relative skin-to-bone distances with the AR stylus and comparing to CT, and the agreement (sub-mm median difference) is reassuring for their tracing procedures. The paper is honest about its limitations and does not oversell; it frames the result as approaching moderate-risk thresholds, not replacing navigation.

The soft spot is the one the stress-test flagged, and it does land. The depth-bias correction (Eq. 5) fits a rigid Procrustes transform from AHAT depth points to stylus samples on the patient's skin. The clinical error metric (Sec. 3.2) then traces the overlay and the skin with the same stylus. If the stylus has any systematic pose error, it shifts the corrected depth cloud (and hence the registered overlay) and the skin trace equally, so the measured overlay-to-skin distance is artificially small. The preclinical validation does not break this coupling, because it compares relative distances, which are invariant to a common translation. The paper itself acknowledges 'small systematic biases from the tool trajectory and from the AHAT depth itself' but never quantifies them. Without an independent absolute reference (e.g., an optical tracker fixed in the room, or a known geometry not used in registration), the 3-4 mm median could understate the true error by an unknown amount.

There is a second, related issue: the pipeline is not fully markerless in the strict sense. The stylus is used to sample the patient's skin to correct depth bias. That is a manual point-based step, which is fine and pragmatic, but it should be reported as such. The 'markerless' claim is a bit strong.

Minor: the statistical comparison (feet vs leg) uses a two-sample permutation test on pooled points, which ignores within-patient/trial correlation. With only 2-3 trials per anatomy, the p-value is overconfident. That's a fixable analysis issue.

Bottom line: the clinical dataset is a useful contribution, and the idea is plausible, but the headline accuracy claim is not yet supported by an independent measurement. A serious referee should ask for an external absolute-error validation (even one trial with a tracked reference) and for a revised statistical analysis. I would not desk-reject this; it deserves a chance in revision. It is a good reading-group paper for discussing measurement circularity in clinical AR.

Referee Report

3 major / 5 minor

Summary. The paper presents a depth-only, markerless AR registration pipeline on HoloLens 2 for surgical guidance, evaluated clinically on feet, ear, and lower leg during live fibula free-flap harvest and mandibular reconstruction. The pipeline comprises per-ROI depth-bias correction (Eq. 5), human-in-the-loop initialization, coarse global alignment (TEASER++), and fine ICP refinement. Preclinical validation shows sub-millimeter agreement between AR-traced skin-to-bone relative distances and CT ground truth. The clinical evaluation reports a pooled median per-point error of 3.9 mm (anatomy-specific medians 3.2–5.3 mm) with 5-mm surface coverage ranging from 72% to 95%, and concludes that these results approach clinical thresholds for moderate-risk tasks without fiducials.

Significance. If the reported accuracy is reliable, the paper provides a meaningful advance: it is among the first clinical evaluations of a markerless HMD-based registration system on multiple anatomies with real patients, and its reported errors are substantially better than the ~10 mm figures of an earlier feasibility study (ARCUS). The depth-bias correction and global-to-local registration design are practical contributions, and the preclinical validation of the tracing metric is a useful methodological step. However, the central accuracy claim rests on a clinical error metric whose absolute reference is not independently verified, and the 'depth-only/markerless' description is qualified by the use of a tracked stylus for depth correction. These issues need to be addressed before the headline accuracy can be fully accepted.

major comments (3)

[Sec. 3.2 and Eq. (5)]
[Sec. 2.3.2]
[Sec. 3.2 and Table 1]

minor comments (5)

[Abstract and Sec. 3.2]
[Sec. 2.3.1, Eq. (3)]
[Sec. 2.3.2]
[Sec. 2.3.5 and Sec. 2.1]
[Sec. 3.2, Table 1]

Circularity Check

2 steps flagged · score 6.0 of 10

Clinical error metric uses the same tracked stylus that fits the depth-bias correction; common-mode tool-tracking error cancels, so the ~3-4 mm claim is not an absolute accuracy bound.

fitted input called prediction [Sec. 2.3.2 (Eq. 5) and Sec. 3.2]
"With stylus samples L={ℓ_k} on patient surface ... and nearest depth points {p_k}, we solve ... min Σ ||ℓ_k − (R p_k + t)||^2 ... We then correct all scene points in the ROI ... p_i ← R p_i + t. ... We computed registration error as the nearest-neighbor distance between surface-traced points on the virtual overlay and the corresponding points traced on the patient’s skin in the HoloLens world frame."

The same tracked stylus is used twice: Eq. (5) fits a rigid correction that maps the AHAT depth cloud onto stylus-sampled skin points, and Sec. 3.2 measures overlay-to-skin distance by tracing both surfaces with that stylus. A systematic stylus pose error shifts the stylus samples used in Eq. (5), hence the corrected depth cloud and the registered overlay, by the same amount as it shifts the later skin trace. The common-mode bias cancels in the nearest-neighbor subtraction, so the reported 3.2-5.4 mm medians do not bound absolute overlay displacement; they are residuals after a fit defined by the same probe.
other [Sec. 3.1]
"For every AR skin surface sample x, we computed the shortest Euclidean distance to the traced internal structure, yielding a per-point relative-distance map d_AR(x). Ground truth was obtained from the CT reconstructions by sampling the CT skin surfaces and computing the same shortest distances to the segmented fibula or metatarsals, giving d_CT(x)."

The preclinical validation compares skin-to-bone relative distances, both traced with the same tracked stylus. A common-mode stylus bias cancels when subtracting two traced surfaces, so d_AR is invariant to exactly the offset that Eq. (5) absorbs. The tight agreement with CT (median |Δd| 0.78-0.80 mm) therefore does not validate the stylus as an absolute reference for the clinical overlay-to-skin metric.

full rationale

The registration algorithm itself is a standard coarse-to-fine point-cloud pipeline and is not circular: Eq. (3) aligns the CT model to the corrected depth cloud, and the depth correction is fit to observed stylus samples. However, the headline clinical accuracy claim is not self-contained as an absolute registration error. The stylus that defines the ground-truth skin points for the Sec. 2.3.2 depth-bias correction is the same device used in Sec. 3.2 to trace both the virtual overlay and the patient's skin. Any systematic bias in stylus tracking shifts the correction (and therefore the registered overlay) and the post-registration skin trace by the same amount, canceling in the overlay-to-skin distance. The authors' own Limitations state that 'surface-tracing evaluation can inherit small systematic biases from the tool trajectory and from the AHAT depth itself,' but these are not quantified against an independent absolute reference. The preclinical check in Sec. 3.1 uses relative skin-to-bone distances, which are rigid-invariant and cannot detect such a common-mode offset. Thus the pooled 3.9 mm median error is partially forced by construction: it measures inconsistency not shared between the fit and the trace, rather than absolute registration accuracy. I assign 6 because the claim does not reduce entirely to the fit (per-anatomy differences, coverage, and residual non-common-mode errors retain independent content), but the central accuracy metric is partially circular.

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

The central claim depends on per-case calibration choices (depth-bias transform, ROI radius) and a set of domain assumptions about rigidity, stylus accuracy, and the validity of surface-trace distance as a surrogate for target registration error. No new physical entities are introduced.

free parameters (4)

Per-ROI depth-bias correction rigid transform (R,t) = per-case, unreported
Fitted by Procrustes to tracked-stylus samples (Eq. 5). This transform changes the frame in which both registration and clinical error are computed; if it absorbs tool/sensor bias, reported errors shrink.
ROI radius r = 80-300 mm
Used in Eq. (7) to crop the scene point cloud. The choice is per anatomy/trial and affects which points enter registration; no ablation is reported.
Model decimation parameters v and N_t = v = 1.0-1.5 mm, N_t ~ 5000
Eq. (2) voxel downsample and farthest-point sampling. Standard preprocessing, but the values influence registration resolution.
ICP residual threshold schedule tau_k = 5 mm to 2 mm
Controls outlier rejection in fine alignment (Sec. 2.3.5). No sensitivity analysis is provided.

assumptions (6)

domain assumption Target anatomy is rigid enough for a single SE(3) transform between CT and intraoperative depth
The registration objective (Eq. 3) is rigid. Soft-tissue deformation or pose changes between CT and surgery violate this; the paper acknowledges this only in limitations.
domain assumption AHAT depth bias over an ROI is well modeled by a single rigid transform
Eq. (5) fits a Procrustes transform to stylus samples. Depth bias may vary with angle and surface, but no residual statistics are reported.
domain assumption Tracked stylus positions are an accurate ground-truth surface reference
Borrowed from STTAR [6]. Both depth-bias correction and clinical error measurement rely on this; no independent accuracy check is performed in the OR.
domain assumption CT skin mesh corresponds to the exposed intraoperative skin surface
Swelling, draping, incision, and patient positioning can shift skin relative to CT; no deformable alignment or residual analysis is included.
domain assumption Nearest-neighbor distance between two traced point sets approximates clinically relevant target registration error
Smooth low-curvature surfaces can hide tangential registration errors; no point-based TRE on anatomical landmarks is reported.
ad hoc to paper User initialization places the model within the coarse aligner's convergence basin
Human-in-the-loop initialization is a new component; no failure rate or sensitivity to initial misalignment is reported.

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Cite this review

Pith. "Pith review of Markerless Augmented Reality Registration for Surgical Guidance: A Multi-Anatomy Clinical Accuracy Study." pith.science (2026). https://pith.science/paper/JZ5ZLBLP

@misc{pith2026251102086,
  author       = {Pith},
  title        = {Pith review of: Markerless Augmented Reality Registration for Surgical Guidance: A Multi-Anatomy Clinical Accuracy Study},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JZ5ZLBLP}},
  note         = {Machine review of arXiv:2511.02086}
}

read the original abstract

Purpose: In this paper, we develop and clinically evaluate a depth-only, markerless augmented reality (AR) registration pipeline on a head-mounted display, and assess accuracy across small or low-curvature anatomies in real-life operative settings. Methods: On HoloLens 2, we align Articulated HAnd Tracking (AHAT) depth to Computed Tomography (CT)-derived skin meshes via (i) depth-bias correction, (ii) brief human-in-the-loop initialization, (iii) global and local registration. We validated the surface-tracing error metric by comparing "skin-to-bone" relative distances to CT ground truth on leg and foot models, using an AR-tracked tool. We then performed seven intraoperative target trials (feet x2, ear x3, leg x2) during the initial stage of fibula free-flap harvest and mandibular reconstruction surgery, and collected 500+ data per trial. Results: Preclinical validation showed tight agreement between AR-traced and CT distances (leg: median |Delta d| 0.78 mm, RMSE 0.97 mm; feet: 0.80 mm, 1.20 mm). Clinically, per-point error had a median of 3.9 mm. Median errors by anatomy were 3.2 mm (feet), 4.3 mm (ear), and 5.3 mm (lower leg), with 5 mm coverage 92-95%, 84-90%, and 72-86%, respectively. Feet vs. lower leg differed significantly (Delta median ~1.1 mm; p < 0.001). Conclusion: A depth-only, markerless AR pipeline on HMDs achieved ~3-4 mm median error across feet, ear, and lower leg in live surgical settings without fiducials, approaching typical clinical error thresholds for moderate-risk tasks. Human-guided initialization plus global-to-local registration enabled accurate alignment on small or low-curvature targets, improving the clinical readiness of markerless AR guidance.

Discussion (0). Continue with ORCID to comment.

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Siddiqi, A., Horan, T., Molloy, R.M., Bloomfield, M.R., Patel, P.D., Piuzzi, N.S.: A clinical review of robotic navigation in total knee arthroplasty: historical systems to modern design. EFORT Open Reviews6(4), 252–269 (2021)

2021