Point-Unet: A Context-Aware Point-Based Neural Network for Volumetric Segmentation

@article{Ho2022PointUnetAC,
  title={Point-Unet: A Context-Aware Point-Based Neural Network for Volumetric Segmentation},
  author={Ngoc-Vuong Ho and Tan H. Nguyen and Gia-Han Diep and Ngan T. H. Le and Binh-Son Hua},
  journal={ArXiv},
  year={2022},
  volume={abs/2203.08964}
}
Medical image analysis using deep learning has recently been prevalent, showing great performance for various downstream tasks including medical image segmentation and its sibling, volumetric image segmentation. Particularly, a typical volumetric segmentation network strongly relies on a voxel grid representation which treats volumetric data as a stack of individual voxel ‘slices’, which allows learning to segment a voxel grid to be as straightforward as extending existing image-based… 
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7 Citations
Background Citations
5
Methods Citations
1

7 Citations

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Super-Resolution Based Patch-Free 3D Medical Image Segmentation with Self-Supervised Guidance

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3DConvCaps: 3DUnet with Convolutional Capsule Encoder for Medical Image Segmentation

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EmbryosFormer: Deformable Transformer and Collaborative Encoding-Decoding for Embryos Stage Development Classification

EmbryosFormer, a computational model to automatically de-tect and classify cell divisions from original time-lapse images, is proposed, designed as an encoder-decoder deformable transformer with collaborative heads.

Brain tumor segmentation based on the fusion of deep semantics and edge information in multimodal MRI

SS-3DCAPSNET: Self-Supervised 3d Capsule Networks for Medical Segmentation on Less Labeled Data

This work extends capsule networks for volumetric medical image segmentation with self-supervised learning for capsule networks pre-training to improve on the problem of weight initialization compared to previous capsule networks.

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