# Medical Image Segmentation with 3D Convolutional Neural Networks: A Survey

arXiv: https://arxiv.org/abs/2108.08467 · HTML: https://mlsanity.com/paper/2108.08467/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 66 of its 135 references that are in the map.
- First posted on arXiv 19 Aug 2021.

## Builds on (most cited first, 30 of 66)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020)
- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs](https://mlsanity.com/paper/1412.7062/) (2014)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [Learning Deconvolution Network for Semantic Segmentation](https://mlsanity.com/paper/1505.04366/) (2015)
- [Med3D: Transfer Learning for 3D Medical Image Analysis](https://mlsanity.com/paper/1904.00625/) (2019)
- [Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images](https://mlsanity.com/paper/1808.08114/) (2018)
- [Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation](https://mlsanity.com/paper/1603.05959/) (2016)
- [Generative Adversarial Network in Medical Imaging: A Review](https://mlsanity.com/paper/1809.07294/) (2018)
- [Semi-Supervised Learning with Ladder Networks](https://mlsanity.com/paper/1507.02672/) (2015)
- [A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises](https://mlsanity.com/paper/2008.09104/) (2020)
- [Tversky loss function for image segmentation using 3D fully convolutional deep networks](https://mlsanity.com/paper/1706.05721/) (2017)
- [FractalNet: Ultra-Deep Neural Networks without Residuals](https://mlsanity.com/paper/1605.07648/) (2016)
- [Conditional Random Fields as Recurrent Neural Networks](https://mlsanity.com/paper/1502.03240/) (2015)

## Built on by (most cited first, 16 of 16)

- [EvCenterNet: Uncertainty Estimation for Object Detection using Evidential Learning](https://mlsanity.com/paper/2303.03037/) (2023)
- [Deep learning for automatic head and neck lymph node level delineation provides expert-level accuracy](https://mlsanity.com/paper/2208.13224/) (2022)
- [3D Brain and Heart Volume Generative Models: A Survey](https://mlsanity.com/paper/2210.05952/) (2022)
- [Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes](https://mlsanity.com/paper/2406.11659/) (2024)
- [Deep learning-based neurodevelopmental assessment in preterm infants](https://mlsanity.com/paper/2601.11944/) (2026)
- [[Citation needed] Data usage and citation practices in medical imaging conferences](https://mlsanity.com/paper/2402.03003/) (2024)
- [Estimation and Analysis of Slice Propagation Uncertainty in 3D Anatomy Segmentation](https://mlsanity.com/paper/2403.12290/) (2024)
- [Interpretable Small Training Set Image Segmentation Network Originated from Multi-Grid Variational Model](https://mlsanity.com/paper/2306.14097/) (2023)
- [P-Age: Pexels Dataset for Robust Spatio-Temporal Apparent Age Classification](https://mlsanity.com/paper/2311.02432/) (2023)
- [Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review](https://mlsanity.com/paper/2210.14909/) (2022)
- [Deep Feature Response Discriminative Calibration](https://mlsanity.com/paper/2411.13582/) (2024)
- [Lung Nodule-SSM: Self-Supervised Lung Nodule Detection and Classification in Thoracic CT Images](https://mlsanity.com/paper/2505.15120/) (2025)
- [TK-Mamba: Marrying KAN With Mamba for Text-Driven 3D Medical Image Segmentation](https://mlsanity.com/paper/2505.18525/) (2025)
- [DM-SegNet: Dual-Mamba Architecture for 3D Medical Image Segmentation with Global Context Modeling](https://mlsanity.com/paper/2506.05297/) (2025)
- [ArchShapeNet:An Interpretable 3D-CNN Framework for Evaluating Architectural Shapes](https://mlsanity.com/paper/2506.14832/) (2025)
- [Compositional Cross-Modality Translation via Whole-Volume Multitask Latent Flow Matching](https://mlsanity.com/paper/2608.08135/) (2026)

## Abstract (arXiv)

Computer-aided medical image analysis plays a significant role in assisting medical practitioners for expert clinical diagnosis and deciding the optimal treatment plan. At present, convolutional neural networks (CNN) are the preferred choice for medical image analysis. In addition, with the rapid advancements in three-dimensional (3D) imaging systems and the availability of excellent hardware and software support to process large volumes of data, 3D deep learning methods are gaining popularity in medical image analysis. Here, we present an extensive review of the recently evolved 3D deep learning methods in medical image segmentation. Furthermore, the research gaps and future directions in 3D medical image segmentation are discussed.