mlsanityData to 7 Oct 2026

Comparison of 2D vs. 3D U-Net Organ Segmentation in abdominal 3D CT images

arXiv 2107.04062 ↗First posted 8 Jul 2021eess.IV cs.CV cs.LGcited by 4

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Abstract (arXiv)

A two-step concept for 3D segmentation on 5 abdominal organs inside volumetric CT images is presented. First each relevant organ's volume of interest is extracted as bounding box. The extracted volume acts as input for a second stage, wherein two compared U-Nets with different architectural dimensions re-construct an organ segmentation as label mask. In this work, we focus on comparing 2D U-Nets vs. 3D U-Net counterparts. Our initial results indicate Dice improvements of about 6% at maximum. In this study to our surprise, liver and kidneys for instance were tackled significantly better using the faster and GPU-memory saving 2D U-Nets. For other abdominal key organs, there were no significant differences, but we observe highly significant advantages for the 2D U-Net in terms of GPU computational efforts for all organs under study.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 15 of its 27 references that are in the map. First posted on arXiv 8 Jul 2021.

Builds on

15 of 27 references.

References · 15

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  3. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,182
  4. 2019Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentationcited by 258
  5. 2020U-Net and its variants for medical image segmentation: theory and applicationscited by 169
  6. 2020DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentationcited by 125
  7. 2016Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fieldscited by 43
  8. 2018CU-Net: Coupled U-Netscited by 11
  9. 2021AG-CUResNeSt: A Novel Method for Colon Polyp Segmentationcited by 6
  10. 20203D Bounding Box Detection in Volumetric Medical Image Data: A Systematic Literature Reviewcited by 5
  11. 2020Facial UV Map Completion for Pose-invariant Face Recognition: A Novel Adversarial Approach based on Coupled Attention Residual UNetscited by 4
  12. 2017Model-based Catheter Segmentation in MRI-imagescited by 3
3 more
  1. 2017Interpatient Respiratory Motion Model Transfer for Virtual Reality Simulations of Liver Puncturescited by 1
  2. 2017Population-based Respiratory 4D Motion Atlas Construction and its Application for VR Simulations of Liver Puncturescited by 1
  3. 2018Comparison of U-net-based Convolutional Neural Networks for Liver Segmentation in CTcited by 1

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Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 1.

4 papers.

  1. 2023Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imagingcited by 16
  2. 2025TTTFusion: A Test-Time Training-Based Strategy for Multimodal Medical Image Fusion in Surgical Robotscited by 1
  3. 2024Deep Learning-Based Brain Image Segmentation for Automated Tumour Detectioncited by 0
  4. 2024Less is More: Selective Reduction of CT Data for Self-Supervised Pre-Training of Deep Learning Models with Contrastive Learning Improves Downstream Classification Performancecited by 0