Speckle2Speckle: Unsupervised Learning of Ultrasound Speckle Filtering Without Clean Data

• URL: http://arxiv.org/abs/2208.00402v1
• Date: Sun, 31 Jul 2022 09:17:32 GMT
• Title: Speckle2Speckle: Unsupervised Learning of Ultrasound Speckle Filtering Without Clean Data
• Authors: R\"udiger G\"obl, Christoph Hennersperger, Nassir Navab
• Abstract summary: In ultrasound imaging the appearance of homogeneous regions of tissue is subject to speckle. Most conventional filtering techniques are fairly hand-crafted and often need to be finely tuned to the present hardware, imaging scheme and application. We propose a deep-learning based method for speckle removal without these limitations.
• Score: 48.81200490360736
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In ultrasound imaging the appearance of homogeneous regions of tissue is subject to speckle, which for certain applications can make the detection of tissue irregularities difficult. To cope with this, it is common practice to apply speckle reduction filters to the images. Most conventional filtering techniques are fairly hand-crafted and often need to be finely tuned to the present hardware, imaging scheme and application. Learning based techniques on the other hand suffer from the need for a target image for training (in case of fully supervised techniques) or require narrow, complex physics-based models of the speckle appearance that might not apply in all cases. With this work we propose a deep-learning based method for speckle removal without these limitations. To enable this, we make use of realistic ultrasound simulation techniques that allow for instantiation of several independent speckle realizations that represent the exact same tissue, thus allowing for the application of image reconstruction techniques that work with pairs of differently corrupted data. Compared to two other state-of-the-art approaches (non-local means and the Optimized Bayesian non-local means filter) our method performs favorably in qualitative comparisons and quantitative evaluation, despite being trained on simulations alone, and is several orders of magnitude faster.

Related papers

• Deep Learning Based Speckle Filtering for Polarimetric SAR Images. Application to Sentinel-1 [51.404644401997736]
  We propose a complete framework to remove speckle in polarimetric SAR images using a convolutional neural network. Experiments show that the proposed approach offers exceptional results in both speckle reduction and resolution preservation.
  arXiv  Detail & Related papers  (2024-08-28T10:07:17Z)
• Speckle Noise Reduction in Ultrasound Images using Denoising Auto-encoder with Skip Connection [0.19116784879310028]
  Ultrasound images often contain speckle noise which can lower their resolution and contrast-to-noise ratio. This can make it more difficult to extract, recognize, and analyze features in the images. Researchers have proposed several speckle reduction methods, but no single method takes all relevant factors into account.
  arXiv  Detail & Related papers  (2024-03-05T08:08:59Z)
• LOTUS: Learning to Optimize Task-based US representations [39.81131738128329]
  Anatomical segmentation of organs in ultrasound images is essential to many clinical applications. Existing deep neural networks require a large amount of labeled data for training in order to achieve clinically acceptable performance. In this paper, we propose a novel approach for learning to optimize task-based ultra-sound image representations.
  arXiv  Detail & Related papers  (2023-07-29T16:29:39Z)
• On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis [58.634791552376235]
  Deep Learning (DL) models have achieved state-of-the-art performance in diagnosing multiple diseases using reconstructed images as input. DL models are sensitive to varying artifacts as it leads to changes in the input data distribution between the training and testing phases. We propose to use other normalization techniques, such as Group Normalization and Layer Normalization, to inject robustness into model performance against varying image artifacts.
  arXiv  Detail & Related papers  (2023-06-23T03:09:03Z)
• Stain-invariant self supervised learning for histopathology image analysis [74.98663573628743]
  We present a self-supervised algorithm for several classification tasks within hematoxylin and eosin stained images of breast cancer. Our method achieves the state-of-the-art performance on several publicly available breast cancer datasets.
  arXiv  Detail & Related papers  (2022-11-14T18:16:36Z)
• Sharp-GAN: Sharpness Loss Regularized GAN for Histopathology Image Synthesis [65.47507533905188]
  Conditional generative adversarial networks have been applied to generate synthetic histopathology images. We propose a sharpness loss regularized generative adversarial network to synthesize realistic histopathology images.
  arXiv  Detail & Related papers  (2021-10-27T18:54:25Z)
• A parameter refinement method for Ptychography based on Deep Learning concepts [55.41644538483948]
  coarse parametrisation in propagation distance, position errors and partial coherence frequently menaces the experiment viability. A modern Deep Learning framework is used to correct autonomously the setup incoherences, thus improving the quality of a ptychography reconstruction. We tested our system on both synthetic datasets and also on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.
  arXiv  Detail & Related papers  (2021-05-18T10:15:17Z)
• OT-driven Multi-Domain Unsupervised Ultrasound Image Artifact Removal using a Single CNN [36.574477281492]
  Ultrasound imaging (US) often suffers from distinct image artifacts from various sources. We propose a novel, unsupervised, deep learning approach in which a single neural network can be used to deal with different types of US artifacts. Our algorithm is rigorously derived using an optimal transport (OT) theory for cascaded probability measures.
  arXiv  Detail & Related papers  (2020-07-10T07:11:04Z)
• Pushing the Limit of Unsupervised Learning for Ultrasound Image Artifact Removal [41.10604715789614]
  Deep learning approaches have been successfully used for ultrasound imaging field. In this paper, inspired by the recent theory of unsupervised learning using optimal transport driven cycleGAN (OT-cycleGAN), we investigate applicability of unsupervised deep learning for US artifact removal problems.
  arXiv  Detail & Related papers  (2020-06-26T03:21:56Z)
• Learned Spectral Computed Tomography [0.0]
  We propose a Deep Learning imaging method for Spectral Photon-Counting Computed Tomography. The method takes the form of a two-step learned primal-dual algorithm that is trained using case-specific data. The proposed approach is characterised by fast reconstruction capability and high imaging performance, even in limited-data cases.
  arXiv  Detail & Related papers  (2020-03-09T13:39:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.