EfficientCellSeg: Efficient Volumetric Cell Segmentation Using Context Aware Pseudocoloring

• URL: http://arxiv.org/abs/2204.03014v1
• Date: Wed, 6 Apr 2022 18:02:15 GMT
• Title: EfficientCellSeg: Efficient Volumetric Cell Segmentation Using Context Aware Pseudocoloring
• Authors: Royden Wagner, Karl Rohr
• Abstract summary: We introduce a small convolutional neural network (CNN) for volumetric cell segmentation. Our model is efficient and has an asymmetric encoder-decoder structure with very few parameters in the decoder. Our method achieves top-ranking results, while our CNN model has an up to 25x lower number of parameters than other top-ranking methods.
• Score: 4.555723508665994
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Volumetric cell segmentation in fluorescence microscopy images is important to study a wide variety of cellular processes. Applications range from the analysis of cancer cells to behavioral studies of cells in the embryonic stage. Like in other computer vision fields, most recent methods use either large convolutional neural networks (CNNs) or vision transformer models (ViTs). Since the number of available 3D microscopy images is typically limited in applications, we take a different approach and introduce a small CNN for volumetric cell segmentation. Compared to previous CNN models for cell segmentation, our model is efficient and has an asymmetric encoder-decoder structure with very few parameters in the decoder. Training efficiency is further improved via transfer learning. In addition, we introduce Context Aware Pseudocoloring to exploit spatial context in z-direction of 3D images while performing volumetric cell segmentation slice-wise. We evaluated our method using different 3D datasets from the Cell Segmentation Benchmark of the Cell Tracking Challenge. Our segmentation method achieves top-ranking results, while our CNN model has an up to 25x lower number of parameters than other top-ranking methods. Code and pretrained models are available at: https://github.com/roydenwa/efficient-cell-seg

Related papers

• 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)
• Large-Scale Multi-Hypotheses Cell Tracking Using Ultrametric Contours Maps [1.015920567871904]
  We describe a method for large-scale 3D cell-tracking through a segmentation selection approach. We show that this method achieves state-of-the-art results in 3D images from the cell tracking challenge. Our framework is flexible and supports segmentations from off-the-shelf cell segmentation models.
  arXiv  Detail & Related papers  (2023-08-08T18:41:38Z)
• CellCentroidFormer: Combining Self-attention and Convolution for Cell Detection [4.555723508665994]
  We propose a novel hybrid CNN-ViT model for cell detection in microscopy images. Our centroid-based cell detection method represents cells as ellipses and is end-to-end trainable.
  arXiv  Detail & Related papers  (2022-06-01T09:04:39Z)
• Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation [133.02190910009384]
  We propose a two-stream graph convolutional network (i.e., TSGCN) to handle inter-view confusion between different raw attributes. Our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
  arXiv  Detail & Related papers  (2022-04-19T10:41:09Z)
• Enforcing Morphological Information in Fully Convolutional Networks to Improve Cell Instance Segmentation in Fluorescence Microscopy Images [1.408123603417833]
  We propose a novel cell instance segmentation approach based on the well-known U-Net architecture. To enforce the learning of morphological information per pixel, a deep distance transformer (DDT) acts as a back-bone model. The obtained results suggest a performance boost over traditional U-Net architectures.
  arXiv  Detail & Related papers  (2021-06-10T15:54:38Z)
• CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation [95.51455777713092]
  Convolutional neural networks (CNNs) have been the de facto standard for nowadays 3D medical image segmentation. We propose a novel framework that efficiently bridges a bf Convolutional neural network and a bf Transformer bf (CoTr) for accurate 3D medical image segmentation.
  arXiv  Detail & Related papers  (2021-03-04T13:34:22Z)
• Comparisons among different stochastic selection of activation layers for convolutional neural networks for healthcare [77.99636165307996]
  We classify biomedical images using ensembles of neural networks. We select our activations among the following ones: ReLU, leaky ReLU, Parametric ReLU, ELU, Adaptive Piecewice Linear Unit, S-Shaped ReLU, Swish, Mish, Mexican Linear Unit, Parametric Deformable Linear Unit, Soft Root Sign.
  arXiv  Detail & Related papers  (2020-11-24T01:53:39Z)
• 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)
• Learning to segment clustered amoeboid cells from brightfield microscopy via multi-task learning with adaptive weight selection [6.836162272841265]
  We introduce a novel supervised technique for cell segmentation in a multi-task learning paradigm. A combination of a multi-task loss, based on the region and cell boundary detection, is employed for an improved prediction efficiency of the network. We observe an overall Dice score of 0.93 on the validation set, which is an improvement of over 15.9% on a recent unsupervised method, and outperforms the popular supervised U-net algorithm by at least $5.8%$ on average.
  arXiv  Detail & Related papers  (2020-05-19T11:31:53Z)
• 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)
• 3D medical image segmentation with labeled and unlabeled data using autoencoders at the example of liver segmentation in CT images [58.720142291102135]
  This work investigates the potential of autoencoder-extracted features to improve segmentation with a convolutional neural network. A convolutional autoencoder was used to extract features from unlabeled data and a multi-scale, fully convolutional CNN was used to perform the target task of 3D liver segmentation in CT images.
  arXiv  Detail & Related papers  (2020-03-17T20:20:43Z)

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.