Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.24020

DinoMUSE: A DINOv2-guided Framework for Unsupervised Lateral Motion Estimation in Ultrasound Elastography

Xiyao Meng·Xingyue Wei·Baihua Luo·Bo Peng·Jianwen Luo
View original source ↗Read source document ↗Export metadata ↓

Abstract

Quasi-static ultrasound elastography (USE) is a non-invasive imaging method for assessing soft tissue mechanical properties. Limited by low resolution, low sampling frequency and lack of phase information in the lateral direction, accurate lateral motion estimation remains a critical bottleneck in USE. Existing methods mainly rely on local image matching and physical constraints (such as tissue incompressibility assumption and effective Poisson's ratio), which lack sufficient contextual information. We present DinoMUSE, a DINO-guided mixture-of-experts (MoE) framework for unsupervised lateral motion estimation in USE. DinoMUSE incorporates high-level visual representations learned by DINOv2 into the motion estimation network to provide global contextual guidance. To facilitate effective interaction between DINOv2 features and local matching features, an MoE-based feature-fusion module adaptively transforms and integrates the two representations. Evaluations against NCC, GLUE, RAFT, and sPICTURE on simulation and in-vivo breast datasets show that DinoMUSE substantially improves lateral estimation accuracy while maintaining competitive axial performance. Specifically, DinoMUSE reduces the mean square error (MSE) of lateral displacement and strain estimation by 60% and 65%, respectively, compared with a SOTA algorithm on the simulation dataset. For in-vivo breast data, DinoMUSE achieves axial strain SNR and CNR of 5.70 and 4.82, and lateral strain SNR and CNR of 4.61 and 2.23, compared to 4.11 and 3.32, and 4.57 and 2.13 for the best competing method, alongside improved lesion visualization. These findings indicate the potential of leveraging transferable visual representations and adaptive feature fusion to improve lateral motion estimation in USE. The code is available at xxxxxxxx.

Explore related subjects

eess.IVcs.CV

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Xiyao Meng, Xingyue Wei, Baihua Luo, Bo Peng, Jianwen Luo. 2026-10-16. DinoMUSE: A DINOv2-guided Framework for Unsupervised Lateral Motion Estimation in Ultrasound Elastography. https://arxiv.org/abs/2610.24020

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

SLIM: Semantic-based Low-bitrate Image compression for Machines

In recent years, the demand of image compression models for machine vision has increased dramatically. However, the training frameworks of image compression still focus on the vision of human, preserving unnecessary details and limiting bitrate efficiency for machine vision tasks. In this paper, we propose Semantic-based Low-bitrate Image compression for Machines, termed SLIM. This proposed framework consists of two key components: RoI-focused latent compression and semantic latent enhancement with dual cues. First, the latent compressor is trained to focus on Region-of-Interest (RoI) areas that are semantically and functionally important for machine vision tasks in the latent space, while requiring no explicit masks during inference. Second, to compensate for severe information loss at extremely low bitrates, a diffusion-based semantic decoder enhances the compressed latent with a pretrained diffusion model guided by two complementary cues provided by an RoI-focused latent and text-based semantic guidance. This design preserves RoI details that are critical for machine perception while maintaining semantically coherent and artifact-suppressed non-RoI regions under extremely low bitrates. Experimental results show that SLIM consistently achieves strong performance across diverse machine vision tasks under the same bits per pixel condition, compared to conventional image compression models for machines. Our code is available at https://github.com/mcml-official/SLIM

eess.IV↗
arXiv2026

Severity-Channel Conditioning: An Injection-Level Study for Consumer-Device Deblurring

Motion-deblurring models lose substantial accuracy when transferred from professional cameras to smartphone-derived imagery. This paper studies whether a severity descriptor, partly derived from the paired sharp target, helps adaptation on the iPhoneBlur consumer-device benchmark. Under a shared update budget, input concatenation of a severity map is compared with global feature modulation by capture metadata, spatial feature modulation by the severity map, and a tier-contrastive output objective, together with an unconditioned spectral-loss control, all fine-tuned from the same domain-adapted latent-diffusion baseline. The primary experiment is an oracle diagnostic: target-derived information is supplied at inference. For the evaluated model pair, input concatenation improves overall peak signal-to-noise ratio from 31.23 to 31.30 dB, whereas feature modulation, the contrastive objective, and the spectral-loss control do not exceed the baseline. The gain is positive on 87.2% of test images and on 14 of 15 held-out source videos; this video-level analysis describes the evaluated checkpoint pair and does not establish robustness to retraining, and three training seeds are reported separately. The input-level gain recurs on a transformer backbone. In one exploratory run, zeroing the channel at inference retains most of the gain in peak signal-to-noise ratio but lowers structural similarity below the baseline. A normalized-step diagnostic does not support the expected larger-gradient explanation. The results characterize the evaluated configurations and do not establish injection location as the sole cause, a spatial-locality mechanism, or a reliable deployment advantage.

eess.IV↗
arXiv2026

CryoSplat: Gaussian Splatting for Cryo-EM Homogeneous Reconstruction

As a critical modality for structural biology, cryogenic electron microscopy (cryo-EM) facilitates the determination of macromolecular structures at near-atomic resolution. The core computational task in single-particle cryo-EM is to reconstruct the 3D electrostatic potential of a molecule from noisy 2D projections acquired at unknown orientations. Gaussian mixture models (GMMs) provide a continuous, compact, and physically interpretable representation for molecular density and have recently gained interest in cryo-EM reconstruction. However, existing methods rely on external consensus maps or atomic models for initialization, limiting their use in self-contained pipelines. In parallel, differentiable rendering techniques such as Gaussian splatting have demonstrated remarkable scalability and efficiency for volumetric representations, suggesting a natural fit for GMM-based cryo-EM reconstruction. However, off-the-shelf Gaussian splatting methods are designed for photorealistic view synthesis and remain incompatible with cryo-EM due to mismatches in the image formation physics, reconstruction objectives, and coordinate systems. Addressing these issues, we propose cryoSplat, a GMM-based method that integrates Gaussian splatting with the physics of cryo-EM image formation. In particular, we develop an orthogonal projection-aware Gaussian splatting, with adaptations such as a view-dependent normalization term and FFT-aligned coordinate system tailored for cryo-EM imaging. These innovations enable stable and efficient homogeneous reconstruction directly from raw cryo-EM particle images using random initialization. Experimental results on real datasets validate the effectiveness and robustness of cryoSplat over representative baselines. The code will be released at https://github.com/Chen-Suyi/cryosplat.

eess.IV↗