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

arXiv: https://arxiv.org/abs/2107.04062 · HTML: https://mlsanity.com/paper/2107.04062/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 27 references that are in the map.
- First posted on arXiv 8 Jul 2021.

## Builds on (most cited first, 15 of 15)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation](https://mlsanity.com/paper/1908.10454/) (2019)
- [U-Net and its variants for medical image segmentation: theory and applications](https://mlsanity.com/paper/2011.01118/) (2020)
- [DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation](https://mlsanity.com/paper/2006.04868/) (2020)
- [Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields](https://mlsanity.com/paper/1610.02177/) (2016)
- [CU-Net: Coupled U-Nets](https://mlsanity.com/paper/1808.06521/) (2018)
- [AG-CUResNeSt: A Novel Method for Colon Polyp Segmentation](https://mlsanity.com/paper/2105.00402/) (2021)
- [3D Bounding Box Detection in Volumetric Medical Image Data: A Systematic Literature Review](https://mlsanity.com/paper/2012.05745/) (2020)
- [Model-based Catheter Segmentation in MRI-images](https://mlsanity.com/paper/1705.06712/) (2017)
- [Facial UV Map Completion for Pose-invariant Face Recognition: A Novel Adversarial Approach based on Coupled Attention Residual UNets](https://mlsanity.com/paper/2011.00912/) (2020)
- [Interpatient Respiratory Motion Model Transfer for Virtual Reality Simulations of Liver Punctures](https://mlsanity.com/paper/1707.08554/) (2017)
- [Population-based Respiratory 4D Motion Atlas Construction and its Application for VR Simulations of Liver Punctures](https://mlsanity.com/paper/1712.01893/) (2017)
- [Comparison of U-net-based Convolutional Neural Networks for Liver Segmentation in CT](https://mlsanity.com/paper/1810.04017/) (2018)

## Built on by (most cited first, 5 of 5)

- [Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging](https://mlsanity.com/paper/2308.06534/) (2023)
- [TTTFusion: A Test-Time Training-Based Strategy for Multimodal Medical Image Fusion in Surgical Robots](https://mlsanity.com/paper/2504.20362/) (2025)
- [Deep Learning-Based Brain Image Segmentation for Automated Tumour Detection](https://mlsanity.com/paper/2404.05763/) (2024)
- [Less is More: Selective Reduction of CT Data for Self-Supervised Pre-Training of Deep Learning Models with Contrastive Learning Improves Downstream Classification Performance](https://mlsanity.com/paper/2410.14524/) (2024)
- [Multi-planar 2D-U-Net Segmentation of 3D-CT Abdominal Organs augmented by Spatial Occurrence Maps](https://mlsanity.com/paper/2606.07717/) (2026)

## 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.