mlsanityData to 7 Oct 2026

Lateral Strain Imaging using Self-supervised and Physically Inspired Constraints in Unsupervised Regularized Elastography

arXiv 2212.08740 ↗First posted 16 Dec 2022eess.IV cs.CVcited by 2

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Abstract (arXiv)

Convolutional Neural Networks (CNN) have shown promising results for displacement estimation in UltraSound Elastography (USE). Many modifications have been proposed to improve the displacement estimation of CNNs for USE in the axial direction. However, the lateral strain, which is essential in several downstream tasks such as the inverse problem of elasticity imaging, remains a challenge. The lateral strain estimation is complicated since the motion and the sampling frequency in this direction are substantially lower than the axial one, and a lack of carrier signal in this direction. In computer vision applications, the axial and the lateral motions are independent. In contrast, the tissue motion pattern in USE is governed by laws of physics which link the axial and lateral displacements. In this paper, inspired by Hooke's law, we first propose Physically Inspired ConsTraint for Unsupervised Regularized Elastography (PICTURE), where we impose a constraint on the Effective Poisson's ratio (EPR) to improve the lateral strain estimation. In the next step, we propose self-supervised PICTURE (sPICTURE) to further enhance the strain image estimation. Extensive experiments on simulation, experimental phantom and in vivo data demonstrate that the proposed methods estimate accurate axial and lateral strain maps.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 17 of its 59 references that are in the map. First posted on arXiv 16 Dec 2022.

Builds on

17 of 59 references.

References · 17

  1. 2017PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volumecited by 966
  2. 2017UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Losscited by 202
  3. 2019Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimationcited by 131
  4. 2019SelFlow: Self-Supervised Learning of Optical Flowcited by 128
  5. 2020Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimationcited by 120
  6. 2020MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Maskcited by 117
  7. 2019Learning with Known Operators reduces Maximum Training Error Boundscited by 25
  8. 2020Semi-Supervised Training of Optical Flow Convolutional Neural Networks in Ultrasound Elastographycited by 9
  9. 2021Ultrasound Elasticity Imaging Using Physics-based Models And Learning-based Plug-And-Play Priorscited by 8
  10. 2022Bi-Directional Semi-Supervised Training of Convolutional Neural Networks for Ultrasound Elastography Displacement Estimationcited by 7
  11. 2018Global Ultrasound Elastography Using Convolutional Neural Networkcited by 5
  12. 2020An Unsupervised Approach to Ultrasound Elastography with End-to-end Strain Regularisationcited by 4
5 more
  1. 2022Physically Inspired Constraint for Unsupervised Regularized Ultrasound Elastographycited by 2
  2. 2020Virtual Source Synthetic Aperture for Accurate Lateral Displacement Estimation in Ultrasound Elastographycited by 1
  3. 2021Combining physics-based modeling and deep learning for ultrasound elastographycited by 1
  4. 2021Fast Strain Estimation and Frame Selection in Ultrasound Elastography using Machine Learningcited by 1
  5. 2022Second-Order Ultrasound Elastography with L1-norm Spatial Regularizationcited by 1

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Cited by year

Citing papers per year: 2023: 1; 2024: 1.

2 papers.

  1. 2023DefCor-Net: Physics-Aware Ultrasound Deformation Correctioncited by 8
  2. 2024Denoising Plane Wave Ultrasound Images Using Diffusion Probabilistic Modelscited by 6