Attentive neural cell instance segmentation

@article{Yi2019AttentiveNC,
  title={Attentive neural cell instance segmentation},
  author={Jingru Yi and Pengxiang Wu and Menglin Jiang and Qiaoying Huang and Daniel J. Hoeppner and Dimitris N. Metaxas},
  journal={Medical image analysis},
  year={2019},
  volume={55},
  pages={
          228-240
        }
}
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43 Citations
Highly Influential Citations
4
Background Citations
15
Methods Citations
11
Results Citations
2

43 Citations

Object-Guided Instance Segmentation With Auxiliary Feature Refinement for Biological Images
TLDR
This paper proposes a novel box-based instance segmentation method that first detects the center points of the objects, from which the bounding box parameters are then predicted and an object-guided coarse-to-fine segmentation branch is built along with the detection branch.
An Integration Convolutional Neural Network for Nuclei Instance Segmentation
TLDR
The experiments demonstrate that the proposed nuclei instance segmentation approach outperforms prior state-of-the-art methods, and could be generalized across variety of nuclear type, magnification and imaging modality.
3D cell instance segmentation via point proposals using cellular components
TLDR
The proposed model utilizes the nuclei of cells as point proposal and employ them as positive and negative point proposals and properly predicts cell lines, which are not even well-annotated during training.
A Fast and Accurate Algorithm for Nuclei Instance Segmentation in Microscopy Images
TLDR
This paper joint the detection and segmentation simultaneously, and proposes a fast and accurate box-based nuclei instance segmentation method that outperforms prior state-of-the-art methods, not only on accuracy but also on speed.
Cell Segmentation by Combining Marker-Controlled Watershed and Deep Learning
TLDR
A cell segmentation method for analyzing images of densely clustered cells that combines the strengths of marker-controlled watershed transformation and a convolutional neural network and generalizes well for various data with state-of-the-art performance.
Object-Guided Instance Segmentation for Biological Images
TLDR
A new box-based instance segmentation method that achieves state-of-the-art performances on three biological datasets: cell nuclei, plant phenotyping dataset, and neural cells by locating the object bounding boxes from their center points.
Application of convolutional neural networks towards nuclei segmentation in localization-based super-resolution fluorescence microscopy images
TLDR
Convolutional neural networks are powerful tools capable of accurately and quickly segmenting localization-based super-resolution microscopy images of nuclei and achieved the best results utilizing the Mask R-CNN architecture.
Assessment of deep learning algorithms for 3D instance segmentation of confocal image datasets
TLDR
This paper implemented and quantitatively compared a number of representative DL pipelines for 3D segmentation, alongside a highly efficient non-DL method named MARS, and shows that the DL pipelines have very different levels of accuracy.
Synthetic-to-real: instance segmentation of clinical cluster cells with unlabeled synthetic training
TLDR
A contour constraint instance segmentation framework (CC framework) for cluster cells based on a cluster cell combination enhancement module to alleviate over- and under-segmentation among individual cell-instance boundaries is proposed.
Learning to segment clustered amoeboid cells from brightfield microscopy via multi-task learning with adaptive weight selection
TLDR
A novel supervised technique for cell segmentation in a multitask learning paradigm using 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.
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References

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