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A comparative analysis of deep learning models for lung segmentation on X-ray images

arXiv 2404.06455 ↗First posted 9 Apr 2024eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

Robust and highly accurate lung segmentation in X-rays is crucial in medical imaging. This study evaluates deep learning solutions for this task, ranking existing methods and analyzing their performance under diverse image modifications. Out of 61 analyzed papers, only nine offered implementation or pre-trained models, enabling assessment of three prominent methods: Lung VAE, TransResUNet, and CE-Net. The analysis revealed that CE-Net performs best, demonstrating the highest values in dice similarity coefficient and intersection over union metric.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 48 references that are in the map. First posted on arXiv 9 Apr 2024.

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References · 14

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2019CE-Net: Context Encoder Network for 2D Medical Image Segmentationcited by 219
  3. 2021Deep Learning for Chest X-ray Analysis: A Surveycited by 84
  4. 2017On Pre-Trained Image Features and Synthetic Images for Deep Learningcited by 51
  5. 2020Online 3D Bin Packing with Constrained Deep Reinforcement Learningcited by 35
  6. 2019Continuous Dice Coefficient: a Method for Evaluating Probabilistic Segmentationscited by 25
  7. 2018Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentationcited by 24
  8. 2021Learning Practically Feasible Policies for Online 3D Bin Packingcited by 16
  9. 2020Lung Segmentation from Chest X-rays using Variational Data Imputationcited by 15
  10. 2017Deep Convolutional Neural Networks as Generic Feature Extractorscited by 10
  11. 2022Online 3D Bin Packing Reinforcement Learning Solution with Buffercited by 8
  12. 2023MeROS: SysML-based Metamodel for ROS-based Systemscited by 6
2 more
  1. 2018A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problemcited by 4
  2. 2020An intent-based approach for creating assistive robots' control systemscited by 4

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