Anatomy Might Be All You Need: Forecasting What to Do During Surgery

Alessandro Carretta; Carlo Serra; Diego Mazzatenta; Ender Konukoglu; Gary Sarwin; Luca Regli; Matteo Zoli; Victor Staartjes

REVIEW 4 major objections 4 minor 13 references

Anatomy Might Be All You Need: Forecasting What to Do During Surgery

T0 review · 4 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This paper tries to establish that anatomical structure detections, not past instrument positions alone, are the key signal for forecasting where a surgical tool moves next, with a 60.58% direction accuracy that beats the instrument-only…

desk verdict New task, honest ablation, but the detector-generated labels put a real ceiling on how much we can trust the anatomy advantage. read the letter →

arxiv 2501.18011 v2 pith:P6R7WZS3 submitted 2025-01-29 cs.CV cs.AI

Gary Sarwin , Alessandro Carretta , Victor Staartjes , Matteo Zoli , Diego Mazzatenta , Luca Regli , Carlo Serra , Ender Konukoglu This is my paper · ORCID

classification cs.CVcs.AI

keywords surgicalguidancevisionanatomicaldetectioninstrumenttrajectoryforecastingendoscopicvideopituitarysurgerydirectionclassification

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 sets out to show that knowing where anatomical structures are in an endoscopic surgical video is what makes it possible to forecast where the surgical instrument will move next. It reports a forecasting model that takes 64 frames of bounding-box detections (15 anatomy classes plus the instrument) and predicts the instrument's per-frame box changes over the next 8 or 16 frames; on a four-way direction classification task with large movements, anatomy-plus-instrument input reaches 60.58% accuracy against 50.71% for instrument history alone and roughly 25% for random guessing. The authors stress that no manual trajectory labels are used: both the inputs and the ground-truth labels come from the same object detection network. They frame this as the first attempt at tool-motion forecasting for manually operated surgery, as opposed to robotic surgery where kinematics are available. If the result holds, it would open a path toward intraoperative guidance that answers what to do next rather than only where things are.

What carries the argument

The load-bearing object is the detection sequence $C_t$: for each of the past $s=64$ frames, an object detection network outputs binary presence flags and normalized bounding boxes for 15 anatomical structures plus the instrument. A six-layer transformer encoder with sinusoidal positional encodings consumes this sequence and maps it to a 16-dimensional latent vector, and a single linear decoder turns that latent vector into predicted frame-to-frame changes of the instrument bounding box for the next $f$ frames. The training objective combines L1 loss on the box changes with a cosine-similarity direction loss on the average direction vectors, and performance is measured by classifying the predicted movement into one of four directions. All of this runs without manual trajectory labels: future instrument boxes are obtained by running the same detection network on future frames.

What would settle it

Manually annotate instrument trajectories frame-by-frame on a held-out subset of the same pituitary videos, then rerun the anatomy-plus-instrument and instrument-only models on those labels. If the anatomy advantage over instrument-only shrinks, disappears, or reverses once the ground truth is accurate, the claim that anatomical features are the key to forecasting would be refuted.

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Extended reading notes

Core claim

The central claim is that anatomical structure detections carry the information needed to forecast instrument motion in pituitary surgery videos, and that this signal outperforms instrument-position history alone. In the authors' experiments, the anatomy-plus-instrument model classifies the direction of the next movement (up, down, left, right) with 60.58% accuracy for 8-frame forecasts on movements above a noise threshold, compared with 50.71% for the instrument-only variant. The same advantage appears, though smaller, for 16-frame forecasts. The authors interpret this as evidence that the coupling between anatomy and instrument movement, rather than raw video appearance or simple motion extrapolation, is the valuable cue for forecasting.

Load-bearing premise

The assumed ground truth for instrument motion is whatever the detection network outputs on future frames; if that detector is systematically biased or noisy, the reported accuracies may describe the detector's self-consistency rather than true surgical tool motion.

Editorial extensions

If this is right

Surgical guidance systems could move from scene assessment to next-action suggestion by feeding anatomy detections into a forecaster like this one.
Because trajectory labels are generated automatically by the detector, the training pipeline can be scaled to large unlabeled video archives without expert annotation of instrument paths.
The reported gap between anatomy-aware and instrument-only models suggests that anatomy detection is not a cosmetic addition but the main source of predictive information.
The same encoder-decoder formulation could be extended to 16-frame forecasts and, the authors argue, to autoregressive and multi-instrument settings.

Reading between the lines

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

Beyond the paper, the reported gain should be read with the shared-detector caveat in mind: because the same network produces inputs and labels, part of the advantage could reflect the detector's internal consistency rather than true anatomical understanding.
A direct test of the 'anatomy is the signal' claim would be to shuffle or corrupt the anatomy boxes while keeping instrument history intact; if accuracy barely drops, the model may be relying on a coarse scene prior.
The method should transfer to other endoscopic procedures if their anatomical classes are annotated; a natural next experiment would retrain the same pipeline on sinus, ear, or laparoscopic videos and compare the anatomy gain per procedure.

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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

The genuinely new thing here is the task: forecasting instrument trajectories in manually operated surgeries from video detections alone, without kinematics. The paper is the first to do that, and it frames the problem sensibly as a first step toward next-action guidance. The ablation is also fairly controlled: same labels, same architecture, only the input changes between anatomy+instrument and instrument-only, and the gap is consistent across both forecast horizons and movement thresholds. That is a real empirical finding worth following up.

The soft spot is the one the reader and the stress-test both hit: ground truth is not manual annotation, it is the YOLO detector's own future-frame outputs. The paper says this openly, which I respect, but it never validates those labels against human annotation on the test videos. That matters because the central claim is that anatomy helps forecasting. If the detector's errors are anatomy-correlated—e.g., localization jitter near particular structures—then the anatomy+instrument model has an extra channel to learn those error patterns. In that case the 60.58% versus 50.71% gap is partly predicting the detector, not the surgery. The threshold filtering removes small-magnitude jitter but not systematic, scene-correlated mislocalization. The absence of error bars or significance tests also makes it hard to know whether the 8-frame gap is robust, though the fact that the pattern persists across settings suggests it is not pure noise.

The method itself is simple—a transformer over YOLO detections—so architectural novelty is not the point. The limitations section is honest about the single-instrument assumption and the undifferentiated instrument class. The citation pattern looks reasonable; prior robot-assisted work is cited, and the claim of first-on-manual-surgery is appropriately scoped.

Who gets value from this? Researchers working on surgical video understanding and intraoperative guidance. It is a plausible proof-of-concept that anatomy context helps trajectory forecasting, but the load-bearing assumption—detector outputs as ground truth—needs validation with manual annotations before the quantitative claim is solid. I would send it to peer review: the task is new, the ablation is clean, and the limitation is fixable. A serious referee should ask for manual validation on a subset, confidence intervals, and ideally a detector-accuracy report.

Yes, bring it to reading group—it will generate a good discussion about self-generated labels and what "forecasting" means when the targets come from a model.

Referee Report

4 major / 4 minor

Summary. The paper proposes a transformer-based model that forecasts future changes in surgical instrument bounding-box coordinates from a history of YOLOv7 detections of anatomical structures and instruments in endoscopic pituitary surgery videos. The forecasting targets are not manually annotated; they are bounding-box changes obtained by applying the same detection network to future frames. Experiments compare an anatomy-plus-instrument model with an instrument-only model on a four-class direction classification task, reporting 60.58% versus 50.71% accuracy for 8-frame forecasts at a movement threshold of 0.1, and similar improvements at other thresholds. The authors position this as the first attempt at forecasting instrument motion in manually operated surgeries.

Significance. If the central claim holds, the paper introduces a clinically relevant task and a simple, well-motivated baseline architecture, and the internal ablation (anatomy plus instrument versus instrument only) is a controlled comparison because both models are trained and evaluated on identical labels. The main contribution, however, depends on the validity of the automatically generated labels, and the manuscript does not currently provide the necessary validation. The work is a reasonable first step, but the evidence for the headline claim is incomplete without a demonstration that the reported improvement reflects true instrument motion rather than detector-specific error patterns.

major comments (4)

[Section 2.3 and Section 3.3] The training and evaluation labels are outputs of the same YOLOv7 detector used to construct the inputs: Section 2.3 states that labels for the forecasting videos are 'extracted automatically using the detection network as well,' and Section 3.3 states that ground-truth motion is estimated from bounding boxes 'predicted with the detection network.' The detector is trained on roughly 19,000 sparsely labeled frames, and its accuracy is not reported. Because the anatomy-plus-instrument model receives anatomical detections as inputs, it can learn the detector's anatomy-correlated localization errors and thereby predict future detector outputs more accurately without forecasting true surgical motion. The 60.58% versus 50.71% gap in Table 1 may therefore partly reflect learning the detector, not learning the surgery. Please validate the automatic labels on a test subset with manual instrument annotations, report detector-level accuracy (e.g., mAP and localization error), and show that the anatomy advantage persists when the evaluation is performed against manual ground truth.
[Table 1 and Section 3.3] All reported accuracies are single point estimates with no confidence intervals, significance tests, or multiple-seed variation. This is particularly important for the 16-frame results, where the anatomy-plus-instrument advantage over instrument-only is 55.86% versus 52.17% at threshold 0.1, a difference that may be within run-to-run variability. The test set consists of 17 videos, so samples are not independent; report per-video bootstrap confidence intervals or per-seed standard deviations, and state whether the comparisons are statistically significant.
[Section 3.3 and Table 1] The evaluation thresholds of 0.1 and 0.05 are introduced to filter detector jitter, but the paper does not calibrate these thresholds to the detector's actual jitter distribution or report what fraction of test samples is removed at each threshold. The text says filtering resulted in around 40,000 and 77,000 samples for the 8-frame case, but the total number of test samples is not given. If the threshold preferentially retains large, anatomy-dependent movements, the reported anatomy advantage could be inflated by the filtering procedure. Please report the total sample count and the jitter distribution, and show that the qualitative conclusion is robust to reasonable threshold choices.
[Section 3.2 and Section 3.3] The baseline set is too narrow to support the claim that anatomical features are the key contributor. The only comparison is between anatomy-plus-instrument detections and instrument-only detections, both processed by the same transformer. To establish that the improvement comes specifically from anatomical information rather than from having more input channels or from temporal smoothing of detector outputs, add simple non-learned baselines such as constant-velocity extrapolation of the instrument's bounding-box centers, and consider an ablated model with an equal number of additional non-informative input channels. These baselines are inexpensive and would strengthen the central claim.

minor comments (4)

[Section 2.3, Eq. (1)] The notation for the ground-truth sequence is inconsistent: the text defines ΔbI_{t:t+f} = [ΔbI_t, ..., ΔbI_f]^T, but the loss sums over r = t+1 to t+f, and the per-frame change is defined as b_{m,r} - b_{m,r-1}. Please use a consistent indexing, e.g., ΔbI_{t+1:t+f}.
[Section 3.1] There is a typo in the sentence 'The medical dataset used for consists of 169 videos'; it should read 'The medical dataset used for this study consists of 169 videos.'
[Section 3.2] The choice of the direction-loss weight λ = 0.5 is reported, but no sensitivity analysis is provided. Since the cosine-similarity term is a substantial part of the objective, a small ablation over λ would help establish that the results are not sensitive to this choice.
[Section 2.2] The statement that the YOLO network was trained with identical parameters as in references [10, 12] is vague, especially because reference [10] is not primarily a YOLO paper. Please specify the exact training configuration or cite the original YOLOv7 training setup.

Circularity Check

1 steps flagged · score 6.0 of 10

The forecasting target is the YOLO detector's own future bounding-box outputs, so the reported anatomy advantage is measured inside a self-generated label loop rather than against independently validated instrument motion.

fitted input called prediction [Section 2.3 (Eq. 1) and Section 3.3]
"The labels for these videos are extracted automatically using the detection network as well. For each sequence Sm,t, the corresponding surgical instrument locations in the following f frames, i.e., bI m,t+1:t+f, are determined using the detection network on the corresponding frames, i.e., detection network applied on xt+1:t+f. ... The ground truth motion of the surgical tools was estimated using their bounding box locations predicted with the detection network using the frames as input."

Eq. (1) minimizes ||(bI_m,r - bI_m,r-1) - predicted||, where bI_m,r is the detection network's output on future frames; inputs Ct are also detection-network outputs. The forecasting task is, by construction, predicting the detector's future bounding-box differences, not an independently measured trajectory. The central comparison (60.58% vs 50.71%, Table 1) is evaluated against these same detector-generated labels. Anatomy-dependent detector behavior is accessible to the anatomy-conditioned model, and the detector is not validated against manual annotations. The anatomical benefit is measured inside a self-generated label loop.

full rationale

The paper is transparent that no manual trajectory labels are used: ground truth is generated by a YOLOv7 detector. There is one identifiable reduction: both the forecasting model's inputs and its training/evaluation targets are outputs of the same detection network. Because the detector is fixed and applied to held-out videos, the model is not memorizing its own training labels, so this is not textbook circularity. However, the central claim still reduces to predicting detector outputs, since Eq. (1) defines the ground-truth change as a difference of detection-network outputs and Section 3.3 confirms the evaluation uses the same detector-generated motion. The paper does not report detector accuracy against manual annotations on the test set, so the anatomy-versus-instrument gap cannot be attributed to true surgical motion with certainty. The self-citations for YOLO training details are not load-bearing. Overall score 6: one core prediction reduces by construction to a fitted detector's outputs, making the headline result partially circular.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claim rests on the assumption that YOLOv7 detection outputs are accurate enough to serve as both input representation and ground truth, that a 64-frame window of detections contains sufficient information, and that a single instrument trajectory is a meaningful target even when multiple instruments are present. No independent ground-truth measurements are used to validate these assumptions.

free parameters (4)

lambda (loss weight) = 0.5
Balances the L1 position loss and the cosine direction loss in Eq. (1); no sensitivity analysis is reported.
movement thresholds for evaluation = 0.1 and 0.05 (normalized box units, roughly 192 and 96 pixels at 1920x1280)
Introduced in Section 3.3 to filter out detector jitter; chosen post hoc and used to produce the headline accuracy numbers.
sequence length s = 64 frames
Set without a window-length ablation study (Section 3.2).
transformer hyperparameters = 6 layers, 5 heads, FC 512/256/128, latent 16, 75/150 epochs, warmup 60, lr 1e-4
Architecture and training choices are not justified by experiments beyond a single configuration.

assumptions (5)

domain assumption YOLOv7 detections provide a reliable proxy for instrument and anatomy positions.
The entire pipeline uses detection outputs as both input and ground truth, but no validation against manual annotations is presented (Sections 2.2, 2.3).
domain assumption Future-frame detections are a valid training target for forecasting.
The forecasting objective in Eq. (1) uses detection outputs, not manual labels, as ground truth (Section 2.3).
domain assumption One instrument instance per frame and one instance per anatomical class is a sufficient simplification.
Multiple instruments are grouped into a single class and the model outputs a single trajectory; the authors note this limitation in Section 4.1.
domain assumption The 15 anatomical classes capture the relevant scene for movement prediction.
No analysis is provided on which structures matter or whether additional classes would help.
ad hoc to paper A 64-frame history of detections is sufficient context for forecasting 8 or 16 frames ahead.
Sequence length is set to 64 without a window-size study (Section 3.2).

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Cite this review

Pith. "Pith review of Anatomy Might Be All You Need: Forecasting What to Do During Surgery." pith.science (2026). https://pith.science/paper/P6R7WZS3

@misc{pith2026250118011,
  author       = {Pith},
  title        = {Pith review of: Anatomy Might Be All You Need: Forecasting What to Do During Surgery},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/P6R7WZS3}},
  note         = {Machine review of arXiv:2501.18011}
}

read the original abstract

Surgical guidance can be delivered in various ways. In neurosurgery, spatial guidance and orientation are predominantly achieved through neuronavigation systems that reference pre-operative MRI scans. Recently, there has been growing interest in providing live guidance by analyzing video feeds from tools such as endoscopes. Existing approaches, including anatomy detection, orientation feedback, phase recognition, and visual question-answering, primarily focus on aiding surgeons in assessing the current surgical scene. This work aims to provide guidance on a finer scale, aiming to provide guidance by forecasting the trajectory of the surgical instrument, essentially addressing the question of what to do next. To address this task, we propose a model that not only leverages the historical locations of surgical instruments but also integrates anatomical features. Importantly, our work does not rely on explicit ground truth labels for instrument trajectories. Instead, the ground truth is generated by a detection model trained to detect both anatomical structures and instruments within surgical videos of a comprehensive dataset containing pituitary surgery videos. By analyzing the interaction between anatomy and instrument movements in these videos and forecasting future instrument movements, we show that anatomical features are a valuable asset in addressing this challenging task. To the best of our knowledge, this work is the first attempt to address this task for manually operated surgeries.

Figures

Figures reproduced from arXiv: 2501.18011 by the authors.

**Figure 1.** Overview of the proposed model pipeline. Frames extracted from a surgical video are processed through an object detection network, which identifies and localizes anatomical structures and surgical instruments across a sequence of frames. These detected sequences are then passed into an encoder to extract temporal features. Finally, the forecasting head predicts the bounding box changes of the instrument for the nex… view at source ↗

**Figure 2.** Qualitative comparison of the forecasting models predicting 8 frames. For visualization purposes, only the predicted future bounding box centers are displayed. The yellow crosses indicate predictions closest to the current frame, while the purple crosses represent predictions furthest into the future. Additionally, the anatomy detections for the model that takes these as input are visualized [PITH_FULL_IMAGE:figur… view at source ↗

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Reference graph

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