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arxiv: 2606.01006 · v1 · pith:C3QK4PNLnew · submitted 2026-05-31 · 💻 cs.CV

Automated Erythrocyte Detection and Tracking for Retinal Blood Flow Quantification in Erythrocyte-Mediated Angiography

Chiao-Yi Wang , Havish S Gadde , Yi-Ting Shen , Saige M. Oechsli , Osamah Saeedi , Yang Tao This is my paper

Pith reviewed 2026-06-28 17:33 UTC · model grok-4.3

classification 💻 cs.CV
keywords erythrocyte detectionerythrocyte trackingretinal blood flowerythrocyte-mediated angiographyEMAmedical image analysisRBF quantificationRBF-EMA dataset
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The pith

EMTrack automates detection and tracking of individual erythrocytes in EMA images to enable retinal blood flow quantification.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents EMTrack as a framework to automate the detection and tracking of erythrocytes visualized in erythrocyte-mediated angiography, a technique that images individual red blood cells at the capillary level. Its flow-context module separates moving cells from paused ones during detection, while the topology-aware tracker manages large inter-frame displacements and motion changes. The authors also release the RBF-EMA dataset containing annotated EMA sequences for both tasks. On this dataset the method exceeds baseline performance in detection and tracking accuracy, and the resulting flow measurements indicate feasibility for automated capillary retinal blood flow analysis. This matters because capillary blood flow has been proposed as a biomarker for several ocular diseases, yet manual analysis has limited its practical use.

Core claim

EMTrack outperforms baseline methods both quantitatively and qualitatively on erythrocyte detection and tracking tasks in the RBF-EMA dataset by employing a flow-context module that distinguishes moving from paused cells and a topology-aware tracking strategy that handles large inter-frame displacements and substantial motion variations, thereby supporting automated quantification of retinal blood flow from EMA sequences.

What carries the argument

The flow-context module for detection combined with the topology-aware tracking strategy, which together address paused cells and variable motion in EMA video sequences.

If this is right

  • EMTrack exceeds baseline methods on both quantitative and qualitative measures for erythrocyte detection and tracking within the RBF-EMA dataset.
  • RBF quantification produced by the framework demonstrates strong potential for automated retinal blood flow measurement.
  • The approach fills the prior gap in automated erythrocyte analysis required for capillary-level RBF studies using EMA.
  • The released RBF-EMA dataset supplies a new benchmark for detection and tracking methods in this imaging modality.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Routine automated RBF measurement could support larger clinical studies testing blood flow as a biomarker in specific eye diseases.
  • The same modules for handling paused cells and large displacements may transfer to cell-tracking tasks in other dynamic vascular imaging modalities.
  • Wider adoption of EMA plus automation could shift retinal blood flow assessment from research-only to more routine diagnostic use.
  • Future work could test whether the quantified flow values correlate with disease progression in longitudinal patient data.

Load-bearing premise

The RBF-EMA dataset supplies accurate annotations that reflect real clinical variability in EMA imaging and the chosen baselines are appropriate comparators.

What would settle it

An independent EMA sequence collection recorded under different clinical or imaging conditions where EMTrack does not exceed the same baselines on detection or tracking metrics.

Figures

Figures reproduced from arXiv: 2606.01006 by Chiao-Yi Wang, Havish S Gadde, Osamah Saeedi, Saige M. Oechsli, Yang Tao, Yi-Ting Shen.

Figure 1
Figure 1. Figure 1: Challenges in EMA videos for erythrocyte tracking. Unlike typical cell or par￾ticle tracking scenarios, EMA videos present three main challenges. (a) Visualization of the three challenges: moving and paused cells exhibit highly similar appearances in individual frames (Challenge 1); large inter-frame displacements (Challenge 2); and substantial motion variations, including abrupt speed changes and sharp tu… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the proposed EMTrack framework. EMTrack consists of two main components: FlowContext-DINO for moving cell detection and a topology-aware track￾ing module for cell tracking. FlowContext-DINO enhances a DINO backbone by incor￾porating a Flow Context Module (FCM) to capture temporal information from N = 5 consecutive frames. The topology-aware tracking module leverages vessel segmentation masks to… view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative comparison of different detection methods. Predicted cells are in￾dicated by green boxes. From Tab. 1 and [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparison of tracking results between ByteTrack and our proposed topology-aware tracking method using different detection methods. GT-Det denotes the use of ground-truth detections; Trad-Gau denotes the traditional Gaussian-based detector; DB (ft) denotes deepBlink fine-tuned on our training set. ground-truth detections is superior, suggesting that improvements in detection accuracy directly c… view at source ↗
Figure 5
Figure 5. Figure 5: Average velocities of matched predicted and ground-truth tracks across different vessel types. GT denotes ground truth, and Pred denotes predictions. As shown in the figure, the median velocities of the predictions are close to those of the ground truth across all vessel types. Moreover, the overall distribu￾tions of the predicted average velocities are largely consistent with the ground￾truth distribution… view at source ↗
read the original abstract

Capillary-level retinal blood flow (RBF) has strong potential as a biomarker for various ocular diseases. However, modalities for measuring capillary-level RBF remain limited. Erythrocyte-mediated angiography (EMA), an emerging imaging technique, enables capillary-level RBF measurement by visualizing individual erythrocytes, yet automated erythrocyte detection and tracking, which are essential for quantifying blood flow, remain largely unexplored. To address this gap, we propose EMTrack, a novel framework featuring a flow-context module for erythrocyte detection that distinguishes moving from paused cells and a topology-aware tracking strategy that enables tracking under large inter-frame displacements and substantial motion variations. In addition, we establish RBF-EMA, a new EMA dataset with comprehensive erythrocyte detection and tracking annotations. Experimental results demonstrate that our method outperforms baseline methods both quantitatively and qualitatively on detection and tracking tasks in the RBF-EMA dataset. Moreover, RBF quantification results highlight the strong potential of our framework for automated retinal blood flow measurement.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes EMTrack, a framework for erythrocyte detection and tracking in EMA images featuring a flow-context module (to distinguish moving vs. paused cells) and topology-aware tracking (for large displacements). It introduces the new RBF-EMA dataset with detection/tracking annotations and claims quantitative and qualitative outperformance over baselines on detection/tracking tasks, plus potential for automated RBF quantification.

Significance. If the results hold with validated ground truth, the work could enable scalable capillary-level RBF measurement as a biomarker for ocular diseases; the new dataset would be a useful resource for the community if annotation quality is documented.

major comments (2)
  1. [Dataset section (likely §4)] Dataset section (likely §4): RBF-EMA is the sole basis for the quantitative superiority claims, yet no inter-annotator agreement, annotation protocol, number of annotators, exclusion criteria, or external clinical validation is reported. This directly affects whether measured gains over baselines reflect true method improvement or annotation artifacts.
  2. [Abstract and §5 (Experiments)] Abstract and §5 (Experiments): The central claim of outperformance is stated without any numeric metrics, error bars, dataset split sizes, or baseline implementation details in the provided abstract; if the full experimental section similarly omits these, the empirical support for the framework's advantage is insufficient.
minor comments (1)
  1. [Method description] Clarify any notation for the flow-context module and topology constraints so that the method can be reproduced from the text alone.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which highlight important aspects of dataset documentation and experimental reporting. We address each major comment below and will revise the manuscript to improve clarity and completeness.

read point-by-point responses

  1. Referee: Dataset section (likely §4): RBF-EMA is the sole basis for the quantitative superiority claims, yet no inter-annotator agreement, annotation protocol, number of annotators, exclusion criteria, or external clinical validation is reported. This directly affects whether measured gains over baselines reflect true method improvement or annotation artifacts.

    Authors: We agree these details are essential for assessing annotation quality. In the revised manuscript, we will expand the dataset section to describe the annotation protocol, number of annotators, inter-annotator agreement (e.g., pairwise IoU and agreement rates), exclusion criteria, and any external clinical review steps performed. This addition will strengthen confidence that performance gains reflect methodological improvements rather than annotation artifacts. revision: yes

  2. Referee: Abstract and §5 (Experiments): The central claim of outperformance is stated without any numeric metrics, error bars, dataset split sizes, or baseline implementation details in the provided abstract; if the full experimental section similarly omits these, the empirical support for the framework's advantage is insufficient.

    Authors: The full experimental section (§5) already reports quantitative metrics (with error bars/standard deviations), dataset split sizes, and baseline implementation details. However, we concur that the abstract would be strengthened by including representative numeric results. We will revise the abstract to incorporate key metrics such as detection precision/recall and tracking accuracy scores to better support the outperformance claims. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical evaluation on newly introduced dataset

full rationale

The paper introduces EMTrack (flow-context detection + topology-aware tracking) and the RBF-EMA dataset, then reports quantitative/qualitative outperformance versus baselines on detection and tracking tasks. No derivation chain exists that reduces a claimed result to its own inputs by construction; there are no fitted parameters renamed as predictions, no self-definitional equations, and no load-bearing self-citations. The evaluation is a standard empirical comparison whose validity rests on dataset quality and baseline choice rather than any internal definitional loop.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review performed on abstract only; no explicit free parameters, axioms, or invented entities are stated in the provided text.

pith-pipeline@v0.9.1-grok · 5717 in / 1119 out tokens · 21996 ms · 2026-06-28T17:33:31.165242+00:00 · methodology

discussion (0)

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