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REVIEW 2 major objections 6 minor 45 references

Deep Learning for High Speed Optical Coherence Elastography with a Fiber Scanning Endoscope

T0 review · 2 major / 6 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read A 2.2 mm endoscope combined with end-to-end deep learning can map soft-tissue elasticity in real time, without knowing wave direction.

desk verdict A genuinely new endoscopic elastography probe with a mostly sound validation; the absolute accuracy numbers rest on an unverified ground-truth transfer, so treat them as provisional rather than proven. read the letter →

arxiv 2509.03193 v1 pith:SL6HQ2H4 submitted 2025-09-03 eess.SP

Maximilian Neidhardt , Sarah Latus , Tim Eixmann , Gereon Hüttmann , Alexander Schlaefer This is my paper · ORCID
classification eess.SP
keywords opticalcoherenceelastographyfiberscanningendoscopespatio-temporaldeeplearningDenseNetshearwaveimagingYoung'smodulusmappingminimallyinvasivesurgerypseudo3D+t
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 tries to show that a miniaturized 2.2 mm fiber-scanning endoscope, when paired with a deep learning pipeline, can estimate local soft-tissue elasticity in real time from complex, multi-directional wave fields—something current ultrasound and MRI elastography cannot do during minimally invasive surgery. The authors acquire OCT phase data at 5.05 kHz using a conical scan pattern that captures pseudo-3D information, and feed it into a spatio-temporal DenseNet that directly outputs Young's modulus. On gelatin phantoms spanning 3–15% concentrations, the deep learning approach achieves a mean absolute error of 4.48±3.63 kPa without any estimate of wave direction, compared with 19.75±21.82 kPa for a conventional FFT-based phase-velocity method. The paper also reports successful detection of a stiff inclusion (DICE 0.91±0.03) and demonstrates qualitative elasticity maps on ex-vivo porcine heart tissue, arguing that this combination makes contactless, direction-independent elastography feasible for surgical navigation.

What carries the argument

The load-bearing object is the conical (pseudo 3D+t) scan pattern generated by deflecting the imaging fiber with a piezoelectric tube at 5.05 kHz, yielding a circular trajectory that samples a propagating wave field in three spatial-plus-time dimensions. The second component is a spatio-temporal DenseNet—a real-valued densely connected convolutional network with about 110,000 parameters—that takes phase-difference volumes (depth × lateral × time) and regresses Young's modulus. The network learns the mapping implicitly, absorbing the probe's scan geometry and the unknown wave directions during training on phantoms whose stiffness is labeled by indentation tests, so no explicit velocity estima

What would settle it

Indent the exact phantom regions imaged by the endoscope immediately after scanning and compare the co-located Young's modulus with the network's predictions; if the mean absolute error against those directly measured values substantially exceeds the reported 4.48±3.63 kPa, the phantom-to-phantom transfer assumption fails.

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Extended reading notes

Core claim

The central claim is that a fiber-scanning endoscope with a conical scan pattern, together with an end-to-end spatio-temporal convolutional network, can convert raw optical-coherence phase data of a diffuse multi-frequency wave field directly into quantitative Young's modulus estimates, without knowing the excitation position or wave propagation direction. The paper reports that for pseudo 3D+t scanning the mean absolute error is 4.48±3.63 kPa, roughly a four-fold improvement over the conventional 2D FFT approach (19.75±21.82 kPa), and that the estimates are spatially uniform across the field of view. It further claims this is the first application of a fiber scanning endoscope to shear wave

Load-bearing premise

The Young's modulus labels that train and evaluate the network are measured on separate cross-sectional gelatin cylinders, and the reported errors assume those indentation values exactly match the stiffness of the phantoms actually imaged.

Editorial extensions

If this is right

  • A 2.2 mm endoscope can generate localized elasticity maps at roughly 14 Hz, compatible with continuous scanning during minimally invasive surgery.
  • The method removes the need to know or calibrate the excitation location and wave propagation direction, simplifying probe design and deployment.
  • On phantoms with a stiff inclusion, deep learning estimates segment the inclusion with DICE 0.91±0.03, compared with 0.64±0.10 for the conventional method.
  • Because the network is trained end-to-end, the same architecture could be retrained on clinical labels to perform tissue classification directly, avoiding indentation tests.
  • Ex-vivo porcine heart results suggest the approach can distinguish adipose and muscle tissue, pointing toward surgical navigation applications.

Reading between the lines

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

  • The reported accuracy depends on the assumption that the imaged phantoms have exactly the same stiffness as the separately indented replicate phantoms; a co-located indentation on the imaged samples would be a stronger test of the mapping.
  • Since the network implicitly absorbs the probe's scan geometry and wave-field statistics, changing the scan pattern, probe hand-built imperfections, or excitation frequencies would likely require retraining or fine-tuning before the error figures carry over.
  • The multi-frequency excitation and diffuse wave fields suggest the network may be able to estimate viscoelastic or frequency-dependent properties, not just a single Young's modulus, if the training labels are extended.
  • The approach could transfer to other excitation sources, such as instrument-integrated piezoelectric actuators, as long as the resulting wave fields are captured by the same conical scanning geometry.
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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

2 major / 6 minor

Summary. The paper presents a fiber scanning endoscope (FSE) with a 2.2 mm outer diameter that acquires high-speed OCT images of propagating shear wave fields in either 2D+t (line) or pseudo 3D+t (conical) scan patterns. The authors propose an end-to-end spatio-temporal DenseNet that takes raw phase data (or ST maps) and directly regresses Young's modulus, avoiding the need for explicit wave-direction estimation, triggering, or spatial calibration of the excitation position. They compare this against a conventional FFT-based phase-velocity estimator with and without angle correction, using leave-one-phantom-out cross-validation on homogeneous gelatin phantoms. The headline result is that the pseudo-3D DL method achieves a mean absolute error of 4.48±3.63 kPa, compared with 19.75±21.82 kPa for the conventional FFT method and 11.33±12.78 kPa for the angle-corrected version. A held-out test on inclusion phantoms reports a DICE score of 0.91±0.03, and ex-vivo porcine heart images demonstrate the feasibility of mapping adipose and muscle tissue. The central claim is that a robust, local, contactless, non-directional elasticity estimate is feasible with a miniature FSE.

Significance. If the reported accuracy holds, this is a significant contribution to interventional elastography. The work is, to the best of the reviewer's knowledge, the first application of a fiber scanning endoscope to shear wave OCE, and the deep learning pipeline eliminates several practical bottlenecks of conventional methods: it does not require knowledge of the excitation position, does not need triggered acquisition, and avoids explicit wave-direction reconstruction. The experimental design is a clear strength: the leave-one-phantom-out cross-validation, the held-out inclusion-phantom test, the systematic variation of imaging position with a robot, and the reported real-time inference rate (about 14 Hz) all support the practical feasibility claim. The DL approach also consistently outperforms the conventional baseline, even when the baseline is given the excitation position and a test-set-fitted calibration, which makes the relative comparison conservative and credible. The paper is clearly of interest to the IEEE TMI readership, but the absolute accuracy claims require further validation.

major comments (2)
  1. [Section II-F / III / Table I] The ground-truth Young's moduli used for both training and evaluation come from indentation tests on separately manufactured cylindrical phantoms, not from the phantoms that were actually imaged. The text states this explicitly: 'indentation tests performed on additionally manufactured cylindrical gelatin phantoms containing the same gelatin to water ratio.' Thus the reported MAEs (e.g., 4.48±3.63 kPa) are errors relative to nominal concentration-level labels. Batch-to-batch variation in gelatin preparation, temperature history, or aging could bias every reported error, and the magnitude of this bias is unquantified. Since the absolute error is the paper's headline quantitative claim, this is a load-bearing issue. The authors should either provide co-located mechanical measurements on the imaged phantoms (or samples from the same batch) or explicitly reframe the claims as relative to a c
  2. [Section II-D] The conventional baseline's linear calibration E = k·v + q with k=24.2, q=-16.4 is stated to be obtained by minimizing the absolute error between FFT estimates and indentation values, apparently on the same evaluation data used to report the final errors. This is a test-set calibration, giving the baseline an advantage that the DL method does not receive. While the DL method still outperforms the baseline, the comparison is not on a methodologically equal footing. The calibration should be performed within each cross-validation fold, or at minimum the authors should explicitly state that this calibration is a best-case upper bound for the conventional approach and quantify the sensitivity of the coefficients.
minor comments (6)
  1. [Table II, G15% row] The entry '12.13±79.1' appears to be a typographical error; the standard deviation 79.1 is implausibly large compared with the surrounding values. Likely it should be '12.13±7.91'.
  2. [Figure 7 / Results] The reported precision value '0.94 ± 2.51' for the FFT+AC method is impossible: precision cannot exceed 1, and a standard deviation of 2.51 is invalid for a quantity constrained to [0,1]. This is likely a typo (e.g., 0.94±0.25) and must be corrected.
  3. [Section II-E] The sentence 'As a baseline, we used densely connected neural networks [31]' is vague because the paper immediately describes a custom DenseNet. Please clarify whether the literature baseline refers to the architecture family or a specific prior implementation, and describe any modifications made here.
  4. [Section II-G] The description 'We trained in total four networks for each fold while randomly choosing a phantom from each gelatin concentration for validation' is unclear. It should explain why four networks are trained (e.g., four random validation splits) and how their predictions are combined or selected.
  5. [Section III] The sentence 'apart from the 3% gelatin concentration the MAE is always best when the FSE is operated in pseudo 3D+t scan mode' is contradicted by Table II, where the pseudo 3D+t method has the lowest MAE at all concentrations, including G3%. This should be corrected.
  6. [Section IV] The sentence 'The size of our scan field already represents a typical laparoscopic image size of 53 × 40 mm at a working distance of 120 mm [44]' is confusing, as the scan field diameter is stated as approximately 1.5 mm. Please rephrase to clarify the intended comparison (e.g., the size of the elasticity map, the field of view after mosaicking, or the lateral resolution).

Circularity Check

1 steps flagged · score 2.0 of 10

Minor circularity in the conventional comparator's velocity-to-modulus calibration; the DL elasticity estimates themselves are trained on external indentation labels and validated on held-out phantoms, so the central claim is not circular.

  1. fitted input called prediction [Section II-D (Conventional Phase Velocity Estimation)]
    "Exclusively for the conventional approach, we mapped the estimated velocities v to the Young’s Modulus E by a linear regression approach defined as E = k · v + q. We minimized the absolute error between estimates and define k = 24.2 and q = −16.4. This allows a fair comparison to our deep learning approach."

    The coefficients k and q are obtained by minimizing the absolute error between the linear mapping output and the indentation-derived Young's modulus labels over the same homogeneous-phantom measurements whose errors are then reported in Table II. Therefore the conventional (FFT/FFT+AC) MAE is an in-sample calibration error of a fitted line, not an independent held-out prediction. The comparison 'DL 3D+t 4.48±3.63 kPa vs conventional 19.75±21.82 kPa' therefore pits a cross-validated deep network against a calibration-fitted baseline. Because the fit minimizes error, this bias favors the conventional comparator, so it does not inflate the central DL claim; nevertheless the 'fair comparison' framing is circular in this limited sense.

full rationale

The paper's central derivation is self-contained: the DenseNet is trained end-to-end on OCT phase data from homogeneous gelatin phantoms, with labels provided by uniaxial indentation tests performed on identically prepared cylindrical phantoms (Section II-F), and evaluated with a nested five-fold cross-validation leaving out whole phantoms of each concentration (Section II-G). The inclusion-phantom DICE evaluation is an additional generalization check on data not used in training. The indentation labels are external measurements, not constructed from the network outputs, so the DL error values do not reduce to the training inputs by construction. Self-citations ([7], [8], [14]) support the architecture and indentation protocol, but the present paper actually performs the phantom manufacture, indentation, and training; no uniqueness theorem or ansatz is imported as a load-bearing black box. The one genuine circular step is the conventional comparator's velocity-to-modulus linear regression, which is fitted by minimizing absolute error on the same data whose MAE is later reported. This is a secondary methodological flaw and not the paper's main claim. Separately, the transfer of indentation moduli from separately manufactured phantoms to the imaged phantoms is an unvalidated assumption, but that is an external-validity/correctness issue, not a circular derivation, so it does not raise the circularity score further.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim depends on the assumption that indentation-modulus labels measured on separate same-recipe phantoms are valid targets for the imaged phantom, and that the network learns a transferable phase-to-modulus mapping. No new physical entities are introduced.

free parameters (3)
  • Baseline linear calibration coefficients k, q = k=24.2, q=-16.4
    E = k·v + q maps conventional phase velocity estimates to Young's modulus; coefficients are fit by minimizing absolute error on the same phantom evaluation data (Section II-D).
  • FFT baseline band-pass and failure threshold = center 400 Hz, band 200 Hz, 10% amplitude cutoff, v>10 m/s considered failed
    Chosen to reduce noise in the conventional ST-map k-space analysis (Section II-D).
  • DL training hyperparameters = lr=1e-5, batch=14, epochs=200, Adam, MSE
    Standard training choices for the regression network (Section II-G).
assumptions (4)
  • domain assumption OCT phase differences between successive scan lines represent sub-resolution tissue displacement caused by the propagating wave field
    The entire input representation is the unwrapped phase difference of complex OCT signals (Section II-C); no validation against an independent displacement measurement is provided.
  • domain assumption Same-concentration gelatin phantoms have identical mechanical properties regardless of batch, temperature, and age
    Indentation labels come from separate phantoms than the imaged ones (Section II-F); the 2 h acclimatization protocol is meant to control temperature but does not measure the actual imaged sample's modulus.
  • domain assumption The spatio-temporal DenseNet trained on homogeneous phantoms generalizes to inclusion phantoms and ex-vivo tissue without retraining
    This is the basis of the inclusion DICE results and the porcine feasibility demonstration (Section III); the network is never exposed to inclusion or tissue data during training.
  • domain assumption The robot positioning (repeatability 0.01 mm) and the piezo excitation provide a stable and repeatable wave field across all 70 positions
    Data acquisition at Section II-B assumes the wave field and phantom are stationary during the 106.89 ms recording.

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Cite this review

Pith. "Pith review of Deep Learning for High Speed Optical Coherence Elastography with a Fiber Scanning Endoscope." pith.science (2026). https://pith.science/paper/SL6HQ2H4

@misc{pith2026250903193,
  author       = {Pith},
  title        = {Pith review of: Deep Learning for High Speed Optical Coherence Elastography with a Fiber Scanning Endoscope},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SL6HQ2H4}},
  note         = {Machine review of arXiv:2509.03193}
}
read the original abstract

Tissue stiffness is related to soft tissue pathologies and can be assessed through palpation or via clinical imaging systems, e.g., ultrasound or magnetic resonance imaging. Typically, the image based approaches are not suitable during interventions, particularly for minimally invasive surgery. To this end, we present a miniaturized fiber scanning endoscope for fast and localized elastography. Moreover, we propose a deep learning based signal processing pipeline to account for the intricate data and the need for real-time estimates. Our elasticity estimation approach is based on imaging complex and diffuse wave fields that encompass multiple wave frequencies and propagate in various directions. We optimize the probe design to enable different scan patterns. To maximize temporal sampling while maintaining three-dimensional information we define a scan pattern in a conical shape with a temporal frequency of 5.05 kHz. To efficiently process the image sequences of complex wave fields we consider a spatio-temporal deep learning network. We train the network in an end-to-end fashion on measurements from phantoms representing multiple elasticities. The network is used to obtain localized and robust elasticity estimates, allowing to create elasticity maps in real-time. For 2D scanning, our approach results in a mean absolute error of 6.31+-5.76 kPa compared to 11.33+-12.78 kPa for conventional phase tracking. For scanning without estimating the wave direction, the novel 3D method reduces the error to 4.48+-3.63 kPa compared to 19.75+-21.82 kPa for the conventional 2D method. Finally, we demonstrate feasibility of elasticity estimates in ex-vivo porcine tissue.

Figures

Figures reproduced from arXiv: 2509.03193 by the authors.

Figure 1
Figure 1. Optical Fiber Scanning Endoscope for data acquisition. We acquire image data with a high temporal frequency of a propagating wave field. The imaging fiber is positioned inside a piezo tube which is deflected by sinusoidal amplified signals for data acquisition in 2D+t or pseudo 3D+t scan mode. thereby be directly related to the elasticity of soft tissue. The principle has been intensively studied with ultrasound ima… view at source ↗
Figure 2
Figure 2. CAD Design: Cross-section view of the individual components of the fiber scanning endoscope. All measurements are given in millime￾ters [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Experimental Setup for Data Acquisition: [I] Flow chart with data acquisition steps. [II] Side-view of the experimental setup with a robot (A), FSE (B), and the piezo element for continuous exciting of a wave field (C). [III] Top view of gelatin phantom; the spherical wave field is indicated in red and blue. The green arrow indicates the direction of the propagating waves and parts of the trajectory of the robot are… view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Data Representations: We image propagating waves with our optical fiber endoscope and acquire (a) 2D+t data or (b) pseudo 3D+t data sets. For conventional image data processing, we estimate a space-time map (ST Map) from 2D+t data sets. We perform a 2D FFT and calculat…
Figure 5
Figure 5. Figure 5: The network consists of three main DenseNet blocks [PITH_FULL_IMAGE:figures/full_fig_p004_5.png]
Figure 5
Figure 5. Figure 5: Spatio-Temporal DenseNet: We predict the elasticity from ST Maps, 2D+t or pseudo 3D+t phase data sets. For 2D+t and 3D+t data representations we crop sequences along the temporal axis. The input data set sizes are indicated on the left along the depth, lateral, and tem…
Figure 6
Figure 6. Figure 6: Elasticity Maps of Gelatin Phantoms: Each map represents the mean estimates for all seven phantoms of the individual gelatin concentrations. Estimates are given for the conventional method (columns 1-2) and for our deep learning (DL) approach (columns 3-5). The red cro…
Figure 7
Figure 7. Figure 7: Elasticity Maps of Inclusions Left: Phantom with stiff inclusion (r=10 mm). The top image is used for ground truth (GT) annotation. Right: Elasticity maps of six individual phantoms. The excitation position is denoted as the red ’x’. Top row GT, center row predictions …
Figure 8
Figure 8. Figure 8: Elasticity Maps of Pig Heart Tissue: Left is depicted the experimental setup including the piezoelectric excitation element, the FSE driven by a robot (not depicted) and the outline of the scan field indicated in black. Right, are depicted elasticity maps obtained from…

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Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.