Non-iterative Simultaneous Rigid Registration Method for Serial Sections of Biological Tissue

• URL: http://arxiv.org/abs/2005.04848v1
• Date: Mon, 11 May 2020 03:44:10 GMT
• Title: Non-iterative Simultaneous Rigid Registration Method for Serial Sections of Biological Tissue
• Authors: Chang Shu, Xi Chen, Qiwei Xie, Chi Xiao, Hua Han
• Abstract summary: We propose a novel non-iterative algorithm to simultaneously estimate optimal rigid transformation for serial section images. Our algorithm method is non-iterative, it can simultaneously compute rigid transformation for a large number of serial section images.
• Score: 11.471087682509005
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose a novel non-iterative algorithm to simultaneously estimate optimal rigid transformation for serial section images, which is a key component in volume reconstruction of serial sections of biological tissue. In order to avoid error accumulation and propagation caused by current algorithms, we add extra condition that the position of the first and the last section images should remain unchanged. This constrained simultaneous registration problem has not been solved before. Our algorithm method is non-iterative, it can simultaneously compute rigid transformation for a large number of serial section images in a short time. We prove that our algorithm gets optimal solution under ideal condition. And we test our algorithm with synthetic data and real data to verify our algorithm's effectiveness.

Related papers

• Digital twins enable full-reference quality assessment of photoacoustic image reconstructions [1.5926880411246285]
  No-reference image quality measures are often inadequate, but full-reference measures require access to an ideal reference image.<n>We tackle this problem by using numerical digital twins of tissue-mimicking phantoms and the imaging system to perform a quantitative calibration.
  arXiv  Detail & Related papers  (2025-05-30T12:25:36Z)
• Integrating Generative and Physics-Based Models for Ptychographic Imaging with Uncertainty Quantification [0.0]
  Ptychography is a scanning coherent diffractive imaging technique that enables imaging nanometer-scale features in extended samples. This paper proposes a Bayesian inversion method for ptychography that performs effectively even with less overlap between neighboring scan locations.
  arXiv  Detail & Related papers  (2024-12-14T16:16:37Z)
• Topology-Preserving Downsampling of Binary Images [1.2430809884830318]
  We present a novel discrete optimization-based approach to generate downsampled versions of binary images. The method is guaranteed to have the same topology as the original, measured by the zeroth and first Betti numbers of the black regions.
  arXiv  Detail & Related papers  (2024-07-25T05:30:09Z)
• Optimal Algorithms for the Inhomogeneous Spiked Wigner Model [89.1371983413931]
  We derive an approximate message-passing algorithm (AMP) for the inhomogeneous problem. We identify in particular the existence of a statistical-to-computational gap where known algorithms require a signal-to-noise ratio bigger than the information-theoretic threshold to perform better than random.
  arXiv  Detail & Related papers  (2023-02-13T19:57:17Z)
• MICS : Multi-steps, Inverse Consistency and Symmetric deep learning registration network [5.5115587651470035]
  MICS is a novel deep learning algorithm for medical imaging registration. We focus our algorithm on the respect of different properties: inverse consistency, symmetry and orientation conservation.
  arXiv  Detail & Related papers  (2021-11-23T19:46:22Z)
• Closed-loop Feedback Registration for Consecutive Images of Moving Flexible Targets [4.61174541905193]
  We propose a closed-loop feedback registration algorithm for matching and stitching the deformable printed patterns on a moving flexible substrate. Our results show that our algorithm can find more matching point pairs with a lower root mean squared error (RMSE) compared to other state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2021-10-20T20:31:43Z)
• Machine Learning for Online Algorithm Selection under Censored Feedback [71.6879432974126]
  In online algorithm selection (OAS), instances of an algorithmic problem class are presented to an agent one after another, and the agent has to quickly select a presumably best algorithm from a fixed set of candidate algorithms. For decision problems such as satisfiability (SAT), quality typically refers to the algorithm's runtime. In this work, we revisit multi-armed bandit algorithms for OAS and discuss their capability of dealing with the problem. We adapt them towards runtime-oriented losses, allowing for partially censored data while keeping a space- and time-complexity independent of the time horizon.
  arXiv  Detail & Related papers  (2021-09-13T18:10:52Z)
• Deep Unfolded Recovery of Sub-Nyquist Sampled Ultrasound Image [94.42139459221784]
  We propose a reconstruction method from sub-Nyquist samples in the time and spatial domain, that is based on unfolding the ISTA algorithm. Our method allows reducing the number of array elements, sampling rate, and computational time while ensuring high quality imaging performance.
  arXiv  Detail & Related papers  (2021-03-01T19:19:38Z)
• Learned Block Iterative Shrinkage Thresholding Algorithm for Photothermal Super Resolution Imaging [52.42007686600479]
  We propose a learned block-sparse optimization approach using an iterative algorithm unfolded into a deep neural network. We show the benefits of using a learned block iterative shrinkage thresholding algorithm that is able to learn the choice of regularization parameters.
  arXiv  Detail & Related papers  (2020-12-07T09:27:16Z)
• An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits [129.1029690825929]
  We introduce a novel algorithm improving over the state-of-the-art along multiple dimensions. We establish minimax optimality for any learning horizon in the special case of non-contextual linear bandits.
  arXiv  Detail & Related papers  (2020-10-23T09:12:47Z)
• u-net CNN based fourier ptychography [5.46367622374939]
  We propose a new retrieval algorithm that is based on convolutional neural networks. Experiments demonstrate that our model achieves better reconstruction results and is more robust under system aberrations.
  arXiv  Detail & Related papers  (2020-03-16T22:48:44Z)
• Active Model Estimation in Markov Decision Processes [108.46146218973189]
  We study the problem of efficient exploration in order to learn an accurate model of an environment, modeled as a Markov decision process (MDP) We show that our Markov-based algorithm outperforms both our original algorithm and the maximum entropy algorithm in the small sample regime.
  arXiv  Detail & Related papers  (2020-03-06T16:17:24Z)

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.