Semi supervised segmentation and graph-based tracking of 3D nuclei in time-lapse microscopy

• URL: http://arxiv.org/abs/2010.13343v1
• Date: Mon, 26 Oct 2020 05:09:44 GMT
• Title: Semi supervised segmentation and graph-based tracking of 3D nuclei in time-lapse microscopy
• Authors: S. Shailja, Jiaxiang Jiang, B.S. Manjunath
• Abstract summary: Current state-of-the-art deep learning methods do not result in accurate boundaries when the training data is weakly annotated. This is motivated by the observation that current state-of-the-art deep learning methods do not result in accurate boundaries when the training data is weakly annotated. A 3D U-Net is trained to get the centroid of the nuclei and integrated with a simple linear iterative clustering (SLIC) supervoxel algorithm.
• Score: 10.398295735266212
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel weakly supervised method to improve the boundary of the 3D segmented nuclei utilizing an over-segmented image. This is motivated by the observation that current state-of-the-art deep learning methods do not result in accurate boundaries when the training data is weakly annotated. Towards this, a 3D U-Net is trained to get the centroid of the nuclei and integrated with a simple linear iterative clustering (SLIC) supervoxel algorithm that provides better adherence to cluster boundaries. To track these segmented nuclei, our algorithm utilizes the relative nuclei location depicting the processes of nuclei division and apoptosis. The proposed algorithmic pipeline achieves better segmentation performance compared to the state-of-the-art method in Cell Tracking Challenge (CTC) 2019 and comparable performance to state-of-the-art methods in IEEE ISBI CTC2020 while utilizing very few pixel-wise annotated data. Detailed experimental results are provided, and the source code is available on GitHub.

Related papers

• Bayesian Self-Training for Semi-Supervised 3D Segmentation [59.544558398992386]
  3D segmentation is a core problem in computer vision. densely labeling 3D point clouds to employ fully-supervised training remains too labor intensive and expensive. Semi-supervised training provides a more practical alternative, where only a small set of labeled data is given, accompanied by a larger unlabeled set.
  arXiv  Detail & Related papers  (2024-09-12T14:54:31Z)
• Joint Embedding Self-Supervised Learning in the Kernel Regime [21.80241600638596]
  Self-supervised learning (SSL) produces useful representations of data without access to any labels for classifying the data. We extend this framework to incorporate algorithms based on kernel methods where embeddings are constructed by linear maps acting on the feature space of a kernel. We analyze our kernel model on small datasets to identify common features of self-supervised learning algorithms and gain theoretical insights into their performance on downstream tasks.
  arXiv  Detail & Related papers  (2022-09-29T15:53:19Z)
• TC-Net: Triple Context Network for Automated Stroke Lesion Segmentation [0.5482532589225552]
  We propose a new network, Triple Context Network (TC-Net), with the capture of spatial contextual information as the core. Our network is evaluated on the open dataset ATLAS, achieving the highest score of 0.594, Hausdorff distance of 27.005 mm, and average symmetry surface distance of 7.137 mm.
  arXiv  Detail & Related papers  (2022-02-28T11:12:16Z)
• Unsupervised Learning on 3D Point Clouds by Clustering and Contrasting [11.64827192421785]
  unsupervised representation learning is a promising direction to auto-extract features without human intervention. This paper proposes a general unsupervised approach, named textbfConClu, to perform the learning of point-wise and global features.
  arXiv  Detail & Related papers  (2022-02-05T12:54:17Z)
• Bend-Net: Bending Loss Regularized Multitask Learning Network for Nuclei Segmentation in Histopathology Images [65.47507533905188]
  We propose a novel multitask learning network with a bending loss regularizer to separate overlapped nuclei accurately. The newly proposed multitask learning architecture enhances the generalization by learning shared representation from three tasks. The proposed bending loss defines high penalties to concave contour points with large curvatures, and applies small penalties to convex contour points with small curvatures.
  arXiv  Detail & Related papers  (2021-09-30T17:29:44Z)
• Scaling Neural Tangent Kernels via Sketching and Random Features [53.57615759435126]
  Recent works report that NTK regression can outperform finitely-wide neural networks trained on small-scale datasets. We design a near input-sparsity time approximation algorithm for NTK, by sketching the expansions of arc-cosine kernels. We show that a linear regressor trained on our CNTK features matches the accuracy of exact CNTK on CIFAR-10 dataset while achieving 150x speedup.
  arXiv  Detail & Related papers  (2021-06-15T04:44:52Z)
• Random Features for the Neural Tangent Kernel [57.132634274795066]
  We propose an efficient feature map construction of the Neural Tangent Kernel (NTK) of fully-connected ReLU network. We show that dimension of the resulting features is much smaller than other baseline feature map constructions to achieve comparable error bounds both in theory and practice.
  arXiv  Detail & Related papers  (2021-04-03T09:08:12Z)
• Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images [51.893494939675314]
  We propose a novel weakly supervised segmentation framework based on partial points annotation. We show that our method can achieve competitive performance compared to the fully supervised counterpart and the state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-07-10T15:41:29Z)
• Learning to Optimize Non-Rigid Tracking [54.94145312763044]
  We employ learnable optimizations to improve robustness and speed up solver convergence. First, we upgrade the tracking objective by integrating an alignment data term on deep features which are learned end-to-end through CNN. Second, we bridge the gap between the preconditioning technique and learning method by introducing a ConditionNet which is trained to generate a preconditioner.
  arXiv  Detail & Related papers  (2020-03-27T04:40:57Z)
• An Auxiliary Task for Learning Nuclei Segmentation in 3D Microscopy Images [6.700873164609009]
  We compare nuclei segmentation algorithms on a database of manually segmented 3d light microscopy volumes. We propose a novel learning strategy that boosts segmentation accuracy by means of a simple auxiliary task. We show that one of our baselines, the popular three-label model, when trained with our proposed auxiliary task, outperforms the recent StarDist-3D.
  arXiv  Detail & Related papers  (2020-02-07T15:47:55Z)

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.