mlsanityData to 7 Oct 2026

Contextual Embedding Learning to Enhance 2D Networks for Volumetric Image Segmentation

arXiv 2404.01723 ↗First posted 2 Apr 2024eess.IV cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

The segmentation of organs in volumetric medical images plays an important role in computer-aided diagnosis and treatment/surgery planning. Conventional 2D convolutional neural networks (CNNs) can hardly exploit the spatial correlation of volumetric data. Current 3D CNNs have the advantage to extract more powerful volumetric representations but they usually suffer from occupying excessive memory and computation nevertheless. In this study we aim to enhance the 2D networks with contextual information for better volumetric image segmentation. Accordingly, we propose a contextual embedding learning approach to facilitate 2D CNNs capturing spatial information properly. Our approach leverages the learned embedding and the slice-wisely neighboring matching as a soft cue to guide the network. In such a way, the contextual information can be transferred slice-by-slice thus boosting the volumetric representation of the network. Experiments on challenging prostate MRI dataset (PROMISE12) and abdominal CT dataset (CHAOS) show that our contextual embedding learning can effectively leverage the inter-slice context and improve segmentation performance. The proposed approach is a plug-and-play, and memory-efficient solution to enhance the 2D networks for volumetric segmentation. Our code is publicly available at https://github.com/JuliusWang-7/CE_Block.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 29 of its 57 references that are in the map. First posted on arXiv 2 Apr 2024.

Builds on

29 of 57 references.

References · 29

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2017Squeeze-and-Excitation Networkscited by 4,763
  3. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  4. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,154
  5. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
  6. 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,509
  7. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  8. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,428
  9. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
  10. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,182
  11. 2015FlowNet: Learning Optical Flow with Convolutional Networkscited by 1,054
  12. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 823
17 more
  1. 2013Network In Networkcited by 600
  2. 2016Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentationcited by 263
  3. 2019MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentationcited by 195
  4. 2018Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentationcited by 180
  5. 2019FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentationcited by 178
  6. 2021UTNet: A Hybrid Transformer Architecture for Medical Image Segmentationcited by 116
  7. 2016DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentationcited by 84
  8. 2018Blazingly Fast Video Object Segmentation with Pixel-Wise Metric Learningcited by 74
  9. 2017Semantic Instance Segmentation via Deep Metric Learningcited by 37
  10. 2016Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentationcited by 25
  11. 2019Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasoundcited by 23
  12. 2016Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentationcited by 16
  13. 2023Dual Cross-Attention for Medical Image Segmentationcited by 15
  14. 2020RAR-U-Net: a Residual Encoder to Attention Decoder by Residual Connections Framework for Spine Segmentation under Noisy Labelscited by 10
  15. 2018Deep Sequential Segmentation of Organs in Volumetric Medical Scanscited by 6
  16. 2023SimPLe: Similarity-Aware Propagation Learning for Weakly-Supervised Breast Cancer Segmentation in DCE-MRIcited by 6
  17. 2022Recurrent Feature Propagation and Edge Skip-Connections for Automatic Abdominal Organ Segmentationcited by 3

Built on by

Not cited yet.