Hierarchical Spatio-temporal Segmentation Network for Ejection Fraction Estimation in Echocardiography Videos

Dongfang Wang; Jian Yang; Tao Zhou; Yizhe Zhang

REVIEW 3 major objections 5 minor 28 references

Hierarchical Spatio-temporal Segmentation Network for Ejection Fraction Estimation in Echocardiography Videos

T0 review · 3 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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

Pith's one-line read HSS-Net: a hierarchical CNN-Mamba video segmentation network that estimates ejection fraction from echocardiography videos more accurately than eight baselines on three datasets.

desk verdict Solid segmentation paper with a real protocol flaw: EF numbers come from oracle-cropped clips (ED first, ES last), so the headline 'EF estimation from videos' overstates what is actually evaluated. read the letter →

arxiv 2508.18681 v1 pith:G3EV6W3O submitted 2025-08-26 cs.CV

Dongfang Wang , Jian Yang , Yizhe Zhang , Tao Zhou This is my paper

classification cs.CV

keywords EchocardiographyEjectionfractionestimationLeftventricularsegmentationVideoMambaStatespacemodelsSpatio-temporalscanMedicalimageanalysis

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 ejection fraction (EF) estimation from echocardiography videos gets more accurate when a network is explicitly split into a local-detail pathway and a global-motion pathway. HSS-Net uses cheap separable convolutions on single frames at low resolution levels, then switches to Mamba-based spatio-temporal blocks that scan across frames and positions to track how the left ventricle deforms over the heartbeat. The paper reports that this division of labour avoids the two failure modes of previous work: image-only methods miss the continuity of cardiac motion, and video-only methods lose fine boundaries that determine volume. On three public video datasets—CAMUS, EchoNet-Pediatric, and EchoNet-Dynamic—HSS-Net reports higher EF correlation and lower bias than eight compared baselines, with per-frame computation of 5.6 GFLOPs at 256×256. A reader should care because EF is the routine clinical measure of systolic function, and small segmentation errors at the base and apex translate into large EF errors.

What carries the argument

The central machinery is the hierarchical encoder-decoder combined with the STCS scanning module. Low-level stages use separable convolution blocks (MobileNetV2-style inverted residuals) on single frames to retain fine local detail. High-level stages use spatio-temporal Mamba blocks that reshape the video into a long one-dimensional sequence and pass it through STCS, which scans along bidirectional temporal, spatial, diagonal, and anti-diagonal routes. This lets a selective state-space model build long-range cross-frame and cross-position dependencies, capturing structures such as apex motion and lateral wall contraction that a single-frame segmentation would miss, while avoiding the full co

What would settle it

Retrain and test HSS-Net on full-length, unaligned echo clips without physician-provided ED/ES alignment, then compare the EF Pearson correlation with the reported aligned numbers (90.47 on CAMUS, 84.50 on EchoNet-Dynamic). If the correlation falls toward the image-only baseline or the bias grows beyond the reported values, the advantage is partly an artifact of the alignment protocol; testing on randomly cropped non-aligned clips would also reveal timing leakage.

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Extended reading notes

Core claim

The paper's central claim is that deliberately separating single-frame detail from multi-frame motion modeling improves ejection-fraction estimation. The proposed HSS-Net keeps convolutional blocks in the low-level encoder stages to preserve edges, textures, and boundary detail, and switches to spatio-temporal Mamba blocks in the high-level stages to capture how the ventricle moves across frames. To keep the multi-frame modeling from staying local, the Spatio-temporal Cross Scan (STCS) module flattens video patches into one-dimensional sequences and scans them along temporal, spatial, diagonal, and anti-diagonal directions in forward and backward passes. On CAMUS, HSS-Net reports a Pearson c

Load-bearing premise

The load-bearing premise is that each input clip is pre-aligned so the end-diastolic frame is the first frame and the end-systolic frame is the last frame, using physician-provided timing; real workflows must discover those two frames from an unaligned video, and the reported EF correlations may depend on having been given that timing.

Editorial extensions

If this is right

Low-level single-frame detail and high-level video modeling are complementary: the paper's ablations show that removing either (image-only or video-only variants) lowers EF correlation on both CAMUS and EchoNet-Dynamic.
Every STCS scan direction contributes: dropping temporal, spatial, diagonal, or anti-diagonal scanning degrades at least one EF metric, which supports the design of multi-perspective scanning.
The model's per-frame cost (5.6 GFLOPs, 31.2M parameters) is in the range of much weaker baselines, suggesting the accuracy gain does not require a large computational budget.
On the two larger datasets the reported EF bias is small (1.29 ± 8.68 on EchoNet-Pediatric, 0.95 ± 6.75 on EchoNet-Dynamic), which is the clinically relevant quantity for screening and follow-up.

Reading between the lines

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

The reported numbers depend on a protocol where each clip is cropped so the physician-annotated end-diastolic frame is first and the end-systolic frame is last; whether the advantage survives without that timing alignment is not tested in the paper and is the most direct open question.
Because STCS's diagonal scanning is a generic mechanism, the same hierarchical CNN-Mamba recipe may transfer to other sparsely annotated video segmentation tasks, such as fetal ultrasound or cardiac MRI, where only two key frames are labelled.
The paper still follows the segmentation-then-EF pipeline; a direct end-to-end regression head on the same spatio-temporal features could potentially capture motion cues that the two-frame volume formula discards.
The reported bias and standard deviation on CAMUS are close to the range of inter-observer variability typically reported in echocardiography, so part of the remaining error may be label noise rather than architecture; an observer-agreement comparison would clarify the practical ceiling.

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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

Two things to know. This is a genuinely solid architectural paper: a hierarchical encoder where low-level stages are separable convolutions and high-level stages are Mamba blocks, with a spatio-temporal cross-scan module that adds diagonal/anti-diagonal scanning. The ablations show each component helps, and the model is efficient (5.6 GFLOPs on CAMUS). Second, the headline EF numbers should not be read as end-to-end EF estimation from raw video, because Section 3 states plainly that every clip is cropped so the ground-truth ED frame is the first and the ground-truth ES frame is the last. The model therefore never has to find the keyframes; it segments the first and last frames of the given clip. That is a different (and easier) task than estimating EF from an unaligned echocardiography video, which is what the title and abstract promise.

What is actually new: the specific combination of convolutional and Mamba stages in this segmentation setup, plus the STCS diagonal modes. This is a legitimate extension of VideoMamba/Vivim, not a breakthrough, but the authors do the work: three public datasets, eight baselines, ablations, and a code link. The relative gains are consistent and, for EF correlation, nontrivial—for instance, CAMUS corr goes from 84.48 for the best baseline to 90.47, and EchoNet-Dynamic from 81.12 to 84.50. Dice gains are small (under a point), but the EF-relevant improvements are larger. Within the aligned-clip protocol, the comparison is fair because all methods see the same clips.

The soft spots. The oracle timing is the load-bearing one. The paper gives no mechanism for locating ED/ES frames in unaligned sequences, and no experiment that removes this assumption. So the absolute correlations and biases are likely inflated relative to a realistic automatic pipeline. This does not invalidate the relative ranking of methods, but it does invalidate the overstatement that HSS-Net 'estimates EF' from videos. Second, there are no confidence intervals or repeated-run variance; some baseline gaps are small enough that significance is unclear. Third, the training loss supervises only the first and last frames of each clip, but the paper does not say whether the unlabeled middle frames are masked out or somehow used; that needs clarification.

Who should read it: people working on echo segmentation and medical video Mamba architectures, and anyone thinking about evaluation protocols for cardiac function estimation. I'd bring it to a reading group and I'd cite it, but with the protocol caveat. It deserves a real referee. Send it out, but the authors should be pushed to either report results on unaligned clips (e.g., using a standard keyframe detector) or reframe the claims as 'segmentation given ED/ES timing,' and to add uncertainty quantification.

Best, [Your name]

Referee Report

3 major / 5 minor

Summary. The paper proposes HSS-Net, a hierarchical spatio-temporal segmentation network for echocardiography videos, combining low-level separable convolution blocks (single-frame detail) with high-level spatio-temporal Mamba blocks (multi-frame global dynamics). A Spatio-temporal Cross Scan (STCS) module with temporal, spatial, diagonal, and anti-diagonal scanning modes is introduced to capture long-range dependencies. EF is computed from predicted ED/ES contours using the Simpson method. The authors report state-of-the-art results on CAMUS, EchoNet-Pediatric, and EchoNet-Dynamic, with correlations of 90.47, 76.91, and 84.50 respectively, along with Dice and HD95 improvements over several segmentation baselines. The code is publicly available.

Significance. If the results hold, HSS-Net offers an efficient (5.6G FLOPs, 31.2M params) hybrid architecture that balances local detail and global temporal modeling, with a credible conceptual motivation for reducing EF bias caused by boundary segmentation errors. The paper ships code and follows a reproducible evaluation protocol, which are strengths. However, the headline EF-estimation claim is weakened by the evaluation protocol: clips are truncated so that the ground-truth ED frame is first and ES frame is last, so the model never discovers keyframe timing. The reported correlations are thus conditional on oracle timing, and the advantage over segmentation baselines is shown on this aligned protocol only. The lack of confidence intervals or significance tests further limits the strength of the SOTA claim.

major comments (3)

[Section 3, Datasets] The evaluation protocol crops every input clip so that the ground-truth ED frame is the first frame and the ground-truth ES frame is the last frame, and only these two frames are supervised and used for EF computation. This oracle timing leaks the keyframe identities that a real EF estimator must discover from unaligned video. The reported correlations (90.47/76.91/84.50) therefore measure segmentation quality on physician-timed keyframes, not end-to-end EF estimation. The comparison to baselines is internally fair, but the absolute SOTA claim does not transfer to unaligned clinical videos. Please add an experiment on unaligned clips (e.g., with a separate ED/ES detector or by evaluating on full-length videos) or explicitly re-frame the claim as keyframe-conditional segmentation.
[Tables 1-2, Section 3.1] All reported numbers are single-run point estimates without confidence intervals, repeated-seed variance, or significance tests. Several Dice gaps are small (e.g., 93.89 vs 93.02 on CAMUS; 92.67 vs 92.48 on EchoNet-Dynamic), so the claimed advantage over baselines may not be statistically reliable. Please report mean±std over at least three runs and apply paired tests (e.g., Wilcoxon or permutation) to the EF correlation and Dice differences.
[Section 3.1, Tables 1-2] The SOTA claim for EF estimation is supported only by comparisons against segmentation baselines (UNet++, TransUNet, VideoMamba, etc.). No direct EF regression or keyframe-detection method is compared, although these methods are the relevant SOTA for EF estimation from videos (e.g., EchoNet). Please include comparisons to such methods under the same aligned protocol and, if possible, on unaligned inputs, or restrict the claim to segmentation-based EF estimation on aligned keyframes.

minor comments (5)

[Table 2] The entry '84.500.95±6.75' in the EchoNet-Dynamic row is missing a space; it should read '84.50 0.95±6.75'.
[Section 1] The sentence 'The primary of echocardiography analysis' is ungrammatical; it should be 'The primary goal of echocardiography analysis' or similar.
[Section 3, Datasets] The text says CAMUS has 'annotations provided for all frames' but later states that only ED and ES annotations are used for training and evaluation. Please clarify which annotations are actually used.
[Throughout] The term 'spatio-temporal' is used with inconsistent hyphenation ('spatio-temporal' vs 'spatiotemporal'). Please unify the notation.
[Equations (1)-(2)] Using the same symbol F_i for both input and output of the residual blocks is acceptable but could be clearer with an explicit out-notation (e.g., F_i^out).

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the EF values are computed from predicted segmentation via the standard Simpson method, and the architecture's empirical claims are tested on external benchmarks.

full rationale

The paper's central claim is that HSS-Net improves ejection-fraction estimation by improving left-ventricular endocardium segmentation. The derivation chain is not circular: the model is trained on ground-truth segmentation masks of ED and ES frames (with Dice and cross-entropy losses), not on EF labels. EF is then computed deterministically from the predicted contours using Simpson's method, as stated in Section 3: 'ground truth and predicted ejection fractions are obtained using the Simpson's single-plane method of disks' (EchoNet) and 'the Simpson's biplane method of disks' (CAMUS). No equation defines the output in terms of itself, and no fitted parameter is later renamed as a prediction. The architectural components are drawn from cited prior work (Mamba, MobileNetV2, etc.), but these citations are not load-bearing in the sense of substituting for empirical validation; the reported correlations, biases, Dice, and HD95 are measured against external benchmark datasets with fixed splits. The authors' own prior papers ([18], [19]) are cited only for general ultrasound-segmentation context, not to justify the uniqueness or validity of the proposed method. The one noteworthy validity concern is the evaluation protocol described in Section 3: 'The video clips are cropped to ensure that the ED frame is the first and the ES frame is the last.' This means the model never has to locate ED/ES frames in unaltered videos, so the absolute SOTA numbers may overstate performance in a fully end-to-end clinical workflow where keyframe timing must be discovered. However, this is an external-validity limitation, not a circularity: the same aligned protocol is applied to all compared methods, the EF labels are not used as training targets, and no step of the derivation reduces to its own input. Therefore the circularity score is 0.

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

The central claim is empirical; it rests on standard clinical formulas, dataset assumptions, and hand-chosen hyperparameters rather than on mathematical derivation. The main nonstandard input is the ED-first/ES-last clip construction, which embeds label timing into the evaluation.

free parameters (3)

Loss balance weight alpha = 0.8
Hand-set weight between Dice loss and binary cross-entropy loss; no sensitivity analysis is reported.
Number of sampled frames T = 10
Uniformly sampled per video following prior work; affects temporal context and computational cost.
ED-first/ES-last clip construction = ED first, ES last
Videos are cropped so ground-truth ED and ES frames become the first and last inputs; this embeds label timing into the input construction.

assumptions (4)

domain assumption Simpson's method of disks converts endocardial contours to volumes and EF.
Standard clinical formula used to derive EF from segmentation masks; if the formula is inaccurate, the EF metrics misstate model quality.
domain assumption Mamba S6 selective state-space models capture long-range spatio-temporal dependencies in video.
The high-level stages rely on this established property of Mamba; the paper provides no independent verification within this work.
domain assumption Ground-truth ED/ES masks are accurate and correspond to true endocardial boundaries.
All training and evaluation depend on the public datasets' annotations being reliable.
domain assumption Uniformly sampled 10-frame clips from ED to ES represent a complete heartbeat cycle.
The evaluation assumes this windowing captures the relevant cardiac dynamics for EF estimation.

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Cite this review

Pith. "Pith review of Hierarchical Spatio-temporal Segmentation Network for Ejection Fraction Estimation in Echocardiography Videos." pith.science (2026). https://pith.science/paper/G3EV6W3O

@misc{pith2026250818681,
  author       = {Pith},
  title        = {Pith review of: Hierarchical Spatio-temporal Segmentation Network for Ejection Fraction Estimation in Echocardiography Videos},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/G3EV6W3O}},
  note         = {Machine review of arXiv:2508.18681}
}

read the original abstract

Automated segmentation of the left ventricular endocardium in echocardiography videos is a key research area in cardiology. It aims to provide accurate assessment of cardiac structure and function through Ejection Fraction (EF) estimation. Although existing studies have achieved good segmentation performance, their results do not perform well in EF estimation. In this paper, we propose a Hierarchical Spatio-temporal Segmentation Network (\ourmodel) for echocardiography video, aiming to improve EF estimation accuracy by synergizing local detail modeling with global dynamic perception. The network employs a hierarchical design, with low-level stages using convolutional networks to process single-frame images and preserve details, while high-level stages utilize the Mamba architecture to capture spatio-temporal relationships. The hierarchical design balances single-frame and multi-frame processing, avoiding issues such as local error accumulation when relying solely on single frames or neglecting details when using only multi-frame data. To overcome local spatio-temporal limitations, we propose the Spatio-temporal Cross Scan (STCS) module, which integrates long-range context through skip scanning across frames and positions. This approach helps mitigate EF calculation biases caused by ultrasound image noise and other factors.

Figures

Figures reproduced from arXiv: 2508.18681 by the authors.

**Figure 1.** Left ventricular segmentation maps for ED and ES frames from A2C and A4C views, with Dice segmentation metrics displayed at the bottom. It is evident that smaller segmentation errors in the key regions (base and apex) within the blue box lead to greater EF calculation deviations. The green, red, and yellow represent the ground truth, prediction, and overlapping regions, respectively. Moreover, physicians typically o… view at source ↗

**Figure 2.** Illustration of the proposed HSS-Net framework, which is a symmetric EncoderDecoder architecture. spatio-temporal cross scan module, which captures dynamic cues from different spatio-temporal perspectives. This module integrates long-range dependencies, such as apex motion changes and the correlation of lateral wall contraction, through a skip-connections-based spatio-temporal scan. Additionally, global dynamic mo… view at source ↗

**Figure 3.** Spatio-temporal cross scan module and its scanning sequence (It denotes the t-th frame in the video clip, and n denotes the number of patch sequences). feature capture within single-frame images. The third and fourth stages are mainly composed of stacked spatio-temporal Mamba blocks for high-level feature capture across multiple frames. The Decoder architecture is symmetric to the Encoder, with a different number of… view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Visualization of segmentation maps for different models. and only the annotations from the ED and ES frames were used for training and evaluation. For the EchoNet-Dynamic dataset, we used the original dataset splits. For the other datasets, we follow related studies [2…

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