Learning Deformable 3D Graph Similarity to Track Plant Cells in Unregistered Time Lapse Images

• URL: http://arxiv.org/abs/2309.11157v2
• Date: Thu, 21 Sep 2023 16:31:25 GMT
• Title: Learning Deformable 3D Graph Similarity to Track Plant Cells in Unregistered Time Lapse Images
• Authors: Md Shazid Islam, Arindam Dutta, Calvin-Khang Ta, Kevin Rodriguez, Christian Michael, Mark Alber, G. Venugopala Reddy, Amit K. Roy-Chowdhury
• Abstract summary: We propose a novel learning-based method that exploits the tightly packed three-dimensional cell structure of plant cells to create a three-dimensional graph in order to perform accurate cell tracking. We demonstrate the efficacy of our algorithm in terms of tracking accuracy and inference-time on a benchmark dataset.
• Score: 17.017730437647977
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tracking of plant cells in images obtained by microscope is a challenging problem due to biological phenomena such as large number of cells, non-uniform growth of different layers of the tightly packed plant cells and cell division. Moreover, images in deeper layers of the tissue being noisy and unavoidable systemic errors inherent in the imaging process further complicates the problem. In this paper, we propose a novel learning-based method that exploits the tightly packed three-dimensional cell structure of plant cells to create a three-dimensional graph in order to perform accurate cell tracking. We further propose novel algorithms for cell division detection and effective three-dimensional registration, which improve upon the state-of-the-art algorithms. We demonstrate the efficacy of our algorithm in terms of tracking accuracy and inference-time on a benchmark dataset.

Related papers

• DiffKillR: Killing and Recreating Diffeomorphisms for Cell Annotation in Dense Microscopy Images [105.46086313858062]
  We introduce DiffKillR, a novel framework that reframes cell annotation as the combination of archetype matching and image registration tasks. We will discuss the theoretical properties of DiffKillR and validate it on three microscopy tasks, demonstrating its advantages over existing supervised, semi-supervised, and unsupervised methods.
  arXiv  Detail & Related papers  (2024-10-04T00:38:29Z)
• Deep Temporal Sequence Classification and Mathematical Modeling for Cell Tracking in Dense 3D Microscopy Videos of Bacterial Biofilms [18.563062576080704]
  We introduce a novel cell tracking algorithm named DenseTrack. DenseTrack integrates deep learning with mathematical model-based strategies to establish correspondences between consecutive frames. We present an eigendecomposition-based cell division detection strategy.
  arXiv  Detail & Related papers  (2024-06-27T23:26:57Z)
• Learning Multimodal Volumetric Features for Large-Scale Neuron Tracing [72.45257414889478]
  We aim to reduce human workload by predicting connectivity between over-segmented neuron pieces. We first construct a dataset, named FlyTracing, that contains millions of pairwise connections of segments expanding the whole fly brain. We propose a novel connectivity-aware contrastive learning method to generate dense volumetric EM image embedding.
  arXiv  Detail & Related papers  (2024-01-05T19:45:12Z)
• Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection [76.11864242047074]
  We propose a novel Affine-Consistent Transformer (AC-Former), which directly yields a sequence of nucleus positions. We introduce an Adaptive Affine Transformer (AAT) module, which can automatically learn the key spatial transformations to warp original images for local network training. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art algorithms on various benchmarks.
  arXiv  Detail & Related papers  (2023-10-22T02:27:02Z)
• Deep Learning Enabled Time-Lapse 3D Cell Analysis [7.094247258573337]
  This paper presents a method for time-lapse 3D cell analysis. We consider the problem of accurately localizing and quantitatively analyzing sub-cellular features. The code is available on Github and the method is available as a service through the BisQue portal.
  arXiv  Detail & Related papers  (2022-08-17T00:07:25Z)
• Search for temporal cell segmentation robustness in phase-contrast microscopy videos [31.92922565397439]
  In this work, we present a deep learning-based workflow to segment cancer cells embedded in 3D collagen matrices. We also propose a geometrical-characterization approach to studying cancer cell morphology. We introduce a new annotated dataset for 2D cell segmentation and tracking, and an open-source implementation to replicate the experiments or adapt them to new image processing problems.
  arXiv  Detail & Related papers  (2021-12-16T12:03:28Z)
• 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)
• Robust 3D Cell Segmentation: Extending the View of Cellpose [0.1384477926572109]
  We extend the Cellpose approach to improve segmentation accuracy on 3D image data. We show how the formulation of the gradient maps can be simplified while still being robust and reaching similar segmentation accuracy.
  arXiv  Detail & Related papers  (2021-05-03T12:47:41Z)
• Image segmentation via Cellular Automata [58.86475603234583]
  We design and train a cellular automaton that can successfully segment high-resolution images. Our smallest automaton uses less than 10,000 parameters to solve complex segmentation tasks.
  arXiv  Detail & Related papers  (2020-08-11T19:04:09Z)
• Limited-angle tomographic reconstruction of dense layered objects by dynamical machine learning [68.9515120904028]
  Limited-angle tomography of strongly scattering quasi-transparent objects is a challenging, highly ill-posed problem. Regularizing priors are necessary to reduce artifacts by improving the condition of such problems. We devised a recurrent neural network (RNN) architecture with a novel split-convolutional gated recurrent unit (SC-GRU) as the building block.
  arXiv  Detail & Related papers  (2020-07-21T11:48:22Z)
• Cell Segmentation and Tracking using CNN-Based Distance Predictions and a Graph-Based Matching Strategy [0.20999222360659608]
  We present a method for the segmentation of touching cells in microscopy images. By using a novel representation of cell borders, inspired by distance maps, our method is capable to utilize not only touching cells but also close cells in the training process. This representation is notably robust to annotation errors and shows promising results for the segmentation of microscopy images containing in the training data underrepresented or not included cell types.
  arXiv  Detail & Related papers  (2020-04-03T11:55:28Z)

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.