Real-Time Brain Tumor Detection in Intraoperative Ultrasound Using YOLO11: From Model Training to Deployment in the Operating Room

Aliasgar Moiyadi; Arianna Barbotti; Brian V. Nahed; Francesco DiMeco; Giuseppe Roberto Giammalva; Ignacio Arrese; Ilyess Zemmoura; Massimiliano Del Bene; Olga Esteban-Sinovas; Prakash Shetty

REVIEW 2 major objections 4 minor 49 references

Real-Time Brain Tumor Detection in Intraoperative Ultrasound Using YOLO11: From Model Training to Deployment in the Operating Room

T0 review · 2 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Real-time YOLO11 object detection can locate brain tumors in intraoperative ultrasound and flag residual tumor during resection, with the best model reaching mAP@50 of 0.95 at 34.16 frames per second.

desk verdict A genuine first deployment of real-time brain tumor detection in intraoperative ultrasound, worth refereeing but the accuracy claims are weakened by pseudo-label circularity in the test set. read the letter →

arxiv 2501.15994 v1 pith:KEI4SVHQ submitted 2025-01-27 eess.IV cs.CV

Santiago Cepeda , Olga Esteban-Sinovas , Roberto Romero , Vikas Singh , Prakash Shetty , Aliasgar Moiyadi , Ilyess Zemmoura , Giuseppe Roberto Giammalva

show 8 more authors

Massimiliano Del Bene Arianna Barbotti Francesco DiMeco Timothy R. West Brian V. Nahed Ignacio Arrese Roberto Hornero Rosario Sarabia

This is my paper · ORCID

classification eess.IVcs.CV

keywords intraoperativeultrasoundbraintumorYOLO11objectdetectionreal-timegliomasurgeryimage-guidedneurosurgerypseudo-labels

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 establish that a real-time object-detection model can make intraoperative ultrasound interpretable enough to guide brain tumor surgery. The authors trained YOLO11 variants on 1,732 ultrasound images from 192 patients, augmented to 11,570 images, and report that the small variant detects tumors with mAP@50 of 0.95 at 34.16 frames per second and 24.9 ms latency. They then deployed the model live in 15 consecutive craniotomies, where surgeons judged the bounding boxes correct and, in one case, the model flagged a residual tumor the surgeon had not initially seen. If true, this would give low-cost ultrasound a computer-vision assist that currently requires expensive intraoperative MRI, and could help surgeons maximize safe resection of gliomas.

What carries the argument

The central object is YOLO11, a single-pass convolutional object detector that divides the image into a grid and predicts bounding boxes and class probabilities directly from the whole image. Its backbone, neck, and head use the C3k2 block, fast spatial pyramid pooling, and a parallel spatial attention module that together provide multi-scale feature extraction cheaply enough for real-time inference. The carrying mechanism for the training data is a pseudo-label pipeline: a prior segmentation model generated tumor outlines for 1,535 of the 1,732 images, and two expert neurosurgeons refined them, so the detector's training target and its test benchmark come from the same label-generation process. Pretrained weights from a large general object dataset and data augmentation, including rotations, flips, shears, brightness shifts, blur, and noise, expand the training set to 11,570 images.

What would settle it

Re-annotate the 359-image test set with independent manual outlines from neurosurgeons who did not produce the pseudo-labels, then recompute mAP@50; if the score drops materially below the reported 0.95, the shared label-generation process inflated the result.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central discovery is that a single-pass detector, the small YOLO11 variant, balances precision and speed well enough for the operating room: mAP@50 of 0.95, mAP@50-95 of 0.65, recall and precision above 0.93, a 24.9 ms latency, and 34.16 frames per second on a laptop GPU. The same training pipeline also produced instance segmentation with a Dice score of 0.88, but the paper treats detection as the primary task because surgeons need to know where the tumor is rather than its exact pixel contour. The prospective deployment showed the model tracking tumors across scanners and tumor types, including meningiomas that were absent from training, and in three patients it detected residual tumor during resection, including one remnant confirmed only after the surgeon re-inspected the cavity.

Load-bearing premise

The reported accuracy assumes that the machine-generated tumor outlines, refined by experts for 1,535 of the 1,732 images, are accurate enough to serve as ground truth for both training and testing; if those outlines carry systematic bias, the mAP numbers overstate real detection performance.

Editorial extensions

If this is right

A laptop with a mid-range GPU can run the detector at 34.16 frames per second, so the system adds no operative delay and requires no expensive dedicated imaging hardware.
The detector can flag residual tumor during resection; in one prospective case it found a remnant that the surgeon initially did not see, suggesting it could increase the extent of resection.
Bounding-box detection, rather than pixel-level segmentation, is sufficient for intraoperative guidance, which lowers the engineering cost of deploying such tools.
The model ignores scanner interface graphics such as menus and buttons in raw video, so it can be connected directly to the ultrasound output without preprocessing.
The detector generalized to tumor types absent from training, such as meningiomas, though for some meningioma cases it included perilesional edema inside the bounding box.

Reading between the lines

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

Editorial inference: because the five YOLO variants score nearly identically on mAP@50, between 0.93 and 0.95, the detector's ceiling is likely set by label fidelity and case diversity rather than architecture size; a testable prediction is that improving the pseudo-labels will raise mAP more than switching to a larger variant.
Editorial inference: adding post-resection images and Doppler-mode images to the training set is a direct next step, since the prospective cases already show the model working on both input types; training on them should sharpen residual-tumor detection specifically.
Editorial inference: the same detection-first design could transfer to other ultrasound-guided procedures where real-time localization is the bottleneck, provided a similarly curated multicenter label pipeline is available.
Editorial inference: a clean way to quantify label-induced bias would be to train and evaluate the same YOLO11 pipeline on a fully manually annotated subset of equal size and compare mAP against the pseudo-label-trained model.

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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

Colleague, the short version: this is a legit engineering study with a real evaluation flaw. The authors deploy a YOLO11 detector for brain tumors in intraoperative ultrasound, run it live in 15 surgeries, and show it runs at 34 FPS on a laptop. That part is new and useful. The headline mAP numbers (0.95 @50, 0.65 @50-95) should be read as optimistic because 1,535 of 1,732 images carry pseudo-labels from the authors' own nnU-Net, and those same pseudo-labels are both the training target and the test benchmark. The expert refinement helps, but there's no inter-observer agreement, no correction statistics, and the nnU-Net was trained on the test patients' own manual slices, so the test set is partly self-agreement. Credit where due: the multicenter dataset (six centers, 197 patients) is a real asset, the YOLO variant comparison is thorough, and the prospective OR work is the first of its kind for this modality. The residual tumor case in patient 11—model flagged something the surgeon hadn't seen, and re-inspection confirmed it—is the kind of anecdote that motivates clinical trials, even though it's n=1. The weaknesses are proportional. The OR evaluation is qualitative and self-assessed by the same team that built the system, so it tells you the workflow doesn't break, not whether the detections are more accurate than the surgeon. The paper's own limitation section mentions manual segmentation variability but doesn't confront the pseudo-label circularity. That's a gap in the argument, and the reviewer should force it open. Bottom line: the real-time feasibility claim holds; the accuracy claim does not, as stated. This deserves a serious referee—it's a genuine first step with a usable dataset and a clear path to external validation—but acceptance should be conditional on fully manual test labels or an independent test cohort, and on tempering the language about accuracy. If you work in surgical ultrasound, cite it for the deployment experience and the dataset, not for the mAP.

Referee Report

2 major / 4 minor

Summary. The paper proposes a real-time brain tumor detection system for intraoperative ultrasound (ioUS) using YOLO11, trained on 1,732 images from the BraTioUS and ReMIND datasets. Most labels are pseudo-labels produced by the authors' earlier nnU-Net segmentation model and subsequently refined by two neurosurgeons; data augmentation expands the training set to 11,570 images. The authors evaluate five YOLO11 variants for both object detection and instance segmentation, reporting for YOLO11s a mAP@50 of 0.95, mAP@50-95 of 0.65, and 34.16 FPS. They also report a prospective deployment in 15 patients, with surgeons qualitatively confirming that real-time predictions delineated tumor regions, including a few residual-tumor detections during resection. The paper claims this is the first real-time object detection implementation for ioUS-guided brain tumor surgery.

Significance. If the quantitative results are correct, the paper would make a useful engineering contribution: it provides a multicenter benchmark for YOLO11 variants on ioUS, demonstrates a low-cost operating-room deployment pipeline, and identifies a clinically relevant use case in residual tumor detection. The authors are explicit that segmentation is secondary, and their bounding-box detection framing is appropriate for real-time surgical workflow. The dataset size and multicenter coverage are strengths. However, the central quantitative claims in Table 3 and Section 6.1 rest on test labels that are not independent of the training-label generation process: 1,535 of 1,732 images have labels generated by the authors' own prior model. The expert refinement reduces but does not quantify the resulting bias. The prospective validation in Section 6.3 is qualitative, unblinded, and self-assessed by the system developers, so it does not independently calibrate the reported mAP values. The paper's own limitation section discusses interobserver variability but does not address the pseudo-label circularity, which is the more serious threat to the reported accuracy.

major comments (2)

[Section 3.2 and Section 5.6 with Table 3] The test-set ground truth is not independent of the label-generation process. The paper states that only 197 of 1,732 images have manual segmentations, while the remaining 1,535 images are pseudo-labels generated by the authors' previously published nnU-Net model and then refined by two neurosurgeons. Because these same pseudo-labels define both the training target and the benchmark for the 359-image test set, any systematic bias in the nnU-Net labels (e.g., consistent inclusion or exclusion of peritumoral edema, or a bias toward particular slice orientations) can be learned by YOLO11 and then counted as correct during evaluation. Moreover, since the nnU-Net was trained on one manual slice per patient and the test patients are drawn from the same cohort, the pseudo-labels for test images inherit patient-specific information that makes them even less like fresh expert annotations. The reported mAP@50 of 0.95 and mAP@50-95 of 0.65 therefore depend critically on an unverified assumption that the expert-refined pseudo-labels are unbiased enough to serve as ground truth. The manuscript should provide a held-out manually annotated test set, or at minimum report inter-observer agreement, correction statistics, and performance stratified by manual versus pseudo-labeled test images.
[Section 6.3 and Table 2] The prospective 'validation' is entirely qualitative and was performed by the same surgeons who built the system. The abstract claims that 'real-time predictions accurately delineated tumor regions,' but the study reports no quantitative metric, no pre-specified evaluation criteria, no blinding, and no independent reference such as postoperative MRI or histopathology. The residual-tumor anecdotes for Patients 2, 11, and 13 are suggestive but uncontrolled and are evaluated by the developers themselves. As written, the prospective results do not independently calibrate the mAP figures and do not establish clinical benefit. The claims of clinical validity should be softened, or the trial design should be extended with protocol-based independent assessment.

minor comments (4)

[Section 5.5] The text says the warmup strategy was implemented 'during the first 3 seasons'; this should read 'epochs.'
[Table 6] The column header 'DCS' is used, but the definition and text use 'DSC' (Dice similarity coefficient). The notation should be consistent.
[Table 3] The recall confidence interval for YOLO11l is reported as (0.95, 1.01), which exceeds the maximum possible value of 1.0 for a proportion. This suggests an issue in the confidence interval computation, or the CI description should specify the resampling method and any clipping convention.
[Section 3.1 and Table 1] The text and table totals are consistent (197 subjects), but the MGH column lists age and sex as NA; please clarify whether these variables are unavailable for all five MGH patients and state this explicitly in the table notes.

Circularity Check

2 steps flagged · score 6.0 of 10

Test-set 'ground truth' for 1,535/1,732 images comes from the authors' own nnU-Net pseudo-labels, so the mAP headline measures agreement with a model-derived reference rather than independent expert annotation.

other [Section 3.2 (Ground truth segmentation) and Section 5.6 (Data split); results in Table 3]
"Using this trained model, we generated pseudo-labels for the remaining 1,535 images in the dataset, which lacked manual segmentations... The pseudo-labels were subsequently reviewed and refined by two neurosurgeons... The dataset of 1,732 images was randomly divided into three subsets: training, validation, and testing, at a ratio of 70:10:20, with stratification by subject."

The headline detection metrics (mAP@50=0.95, mAP@50-95=0.65 in Section 6.1/Table 3) are computed on a test set whose ground-truth boxes are, for the large majority of images, produced by the same pseudo-label generator that created the training targets. Section 3.2 states that one representative slice per patient was used to train the preliminary nnU-Net, and Section 5.6 splits the same 1,732-image dataset by subject; therefore test patients are among the patients whose manual slices the pseudo-label model already saw. YOLO is trained and scored on labels from a single non-independent process, so any systematic bias in the nnU-Net labels (e.g., consistent over- or under-inclusion of peritumoral edema) can be learned by YOLO and then counted as correct during evaluation.
self citation load bearing [Section 3.2 (Ground truth segmentation), references [18] and [33]]
"These slices were used to train a preliminary segmentation model based on the nnU-Net framework [33], previously developed and published by our group. ... Using this trained model, we generated pseudo-labels for the remaining 1,535 images in the dataset, which lacked manual segmentations."

The reliability of the 1,535 pseudo-labels, which constitute the majority of both training and test ground truth, is supported by appeal to the authors' own prior publication [18] (Cepeda et al., Cancers 2025). That prior model is not an external arbiter: it was trained on manual slices from the same 197-patient cohort used in this study, and its output is the very reference against which YOLO11 is evaluated. The self-citation is load-bearing because the central mAP claims would lose their independent grounding if [18]'s segmentations were biased, and the paper provides no fully independent expert re-annotation of the test set to break the circular chain.

full rationale

The paper's central quantitative claim—YOLO11s achieving mAP@50 of 0.95 and mAP@50-95 of 0.65—is partially circular. Section 3.2 reveals that 1,535 of the 1,732 images are labeled by the authors' previously published nnU-Net model, refined by two neurosurgeons, while only 197 images have manual segmentations. Section 5.6 then splits this same 1,732-image dataset into training/validation/test by subject, so the 359-image test set is drawn from the same pseudo-label population used for training. The reported mAP therefore measures YOLO's agreement with model-derived labels, not with fresh independent expert annotation. The circularity is compounded by the fact that the nnU-Net was trained on one manual slice per patient from the same 197 patients, so its pseudo-labels for test subjects inherit patient-specific information. The prospective operating-room evaluation in Section 6.3 is qualitative and was assessed by the same surgeons who built the system; it does not independently calibrate the mAP figures. Section 7.1 lists only interobserver variability in manual segmentation as the main limitation and does not address the pseudo-label test-set dependence, which is a relevant missing limitation. The paper does contain genuine independent content: the YOLO11 variants are compared against each other on identical labels, the speed/latency/FLOPs measurements are objective, and the prospective deployment in 15 patients demonstrates real-time feasibility. Because the central detection-accuracy claim is partially tied to the authors' own label-generation model, a score of 6 is appropriate; the expert refinement and qualitative prospective confirmation prevent it from being fully forced, so 8-10 would be too severe.

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

The quantitative claims depend primarily on the quality of expert-refined pseudo-labels and standard YOLO hyperparameters. No new physical or biological entities are introduced. The most important ledger entry is the pseudo-labeling step, which is not a fitted parameter but a modeling assumption that bears directly on the validity of the test set.

free parameters (4)

Confidence threshold = 0.25
Used for inference to accept detections; directly affects precision and recall values reported in Tables 3 and 6.
NMS IoU threshold = 0.7
Used in non-maximum suppression; affects the number of reported detections and therefore mAP.
Training image size = 640x640
Input resolution for YOLO11; higher resolution would likely change mAP and FPS.
Epochs = 500
Training length; chosen with early stopping not reported, affects final model performance.

assumptions (5)

domain assumption Pseudo-labels refined by two expert neurosurgeons are accurate enough to serve as ground truth for training and evaluation.
Section 3.2 states that pseudo-labels from the preliminary model were reviewed and refined by experts; the entire quantitative evaluation assumes these labels are unbiased.
domain assumption The preliminary nnU-Net model used to generate pseudo-labels is independent of the YOLO11 training and does not leak patient-specific information into the test split.
Section 3.2 explains the pseudo-labeling process; if the nnU-Net saw a representative slice of each patient, then test labels for the same patient may be influenced by the training data of the label generator.
domain assumption The random split by subject prevents any image-level leakage between training and test.
Section 5.6 describes a 70:10:20 split stratified by subject; this is stated but not demonstrated, and the pseudo-label generation step is a separate potential leakage path.
domain assumption Qualitative surgeon assessment is sufficient to establish that the system integrates into the surgical workflow.
Section 6.3 reports the prospective deployment with qualitative judgments; the feasibility claim rests on this assumption.
domain assumption Standard YOLO11 training on COCO-pretrained weights transfers to ultrasound images.
Section 4.3 states the models are initialized with COCO-pretrained weights; no experiments test whether this pretraining helps or hurts on ultrasound.

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Cite this review

Pith. "Pith review of Real-Time Brain Tumor Detection in Intraoperative Ultrasound Using YOLO11: From Model Training to Deployment in the Operating Room." pith.science (2026). https://pith.science/paper/KEI4SVHQ

@misc{pith2026250115994,
  author       = {Pith},
  title        = {Pith review of: Real-Time Brain Tumor Detection in Intraoperative Ultrasound Using YOLO11: From Model Training to Deployment in the Operating Room},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KEI4SVHQ}},
  note         = {Machine review of arXiv:2501.15994}
}

read the original abstract

Intraoperative ultrasound (ioUS) is a valuable tool in brain tumor surgery due to its versatility, affordability, and seamless integration into the surgical workflow. However, its adoption remains limited, primarily because of the challenges associated with image interpretation and the steep learning curve required for effective use. This study aimed to enhance the interpretability of ioUS images by developing a real-time brain tumor detection system deployable in the operating room. We collected 2D ioUS images from the Brain Tumor Intraoperative Database (BraTioUS) and the public ReMIND dataset, annotated with expert-refined tumor labels. Using the YOLO11 architecture and its variants, we trained object detection models to identify brain tumors. The dataset included 1,732 images from 192 patients, divided into training, validation, and test sets. Data augmentation expanded the training set to 11,570 images. In the test dataset, YOLO11s achieved the best balance of precision and computational efficiency, with a mAP@50 of 0.95, mAP@50-95 of 0.65, and a processing speed of 34.16 frames per second. The proposed solution was prospectively validated in a cohort of 15 consecutively operated patients diagnosed with brain tumors. Neurosurgeons confirmed its seamless integration into the surgical workflow, with real-time predictions accurately delineating tumor regions. These findings highlight the potential of real-time object detection algorithms to enhance ioUS-guided brain tumor surgery, addressing key challenges in interpretation and providing a foundation for future development of computer vision-based tools for neuro-oncological surgery.

Figures

Figures reproduced from arXiv: 2501.15994 by the authors.

**Figure 2.** Schematic representation of the workflow data preparation [PITH_FULL_IMAGE:figures/full_fig_p014_2.png] view at source ↗

**Figure 3.** Several examples of tumor detection predictions in the test group. The blue bounding [PITH_FULL_IMAGE:figures/full_fig_p016_3.png] view at source ↗

Figures from the paper (5 more)

**Figure 4.** Figure 4: Several examples of tumor segmentation predictions in the test group. The blue region [PITH_FULL_IMAGE:figures/full_fig_p018_4.png]

**Figure 5.** Figure 5: Operating room setup showing the integration of intraoperative ultrasound imaging, and [PITH_FULL_IMAGE:figures/full_fig_p019_5.png]

**Figure 6.** Figure 6: Example of a raw image captured from the video acquisition system in Case 8, showcasing [PITH_FULL_IMAGE:figures/full_fig_p019_6.png]

**Figure 7.** Figure 7: Real-time predictions of the YOLO11s detection model in prospective cases. The number in [PITH_FULL_IMAGE:figures/full_fig_p020_7.png]

**Figure 8.** Figure 8: Examples of predictions in intraoperative images from prospective cases during surgical [PITH_FULL_IMAGE:figures/full_fig_p021_8.png]

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

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