# Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets

arXiv: https://arxiv.org/abs/1708.00573 · HTML: https://mlsanity.com/paper/1708.00573/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2017.

## Built on by (most cited first, 30 of 32)

- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/2207.14191/) (2022)
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- [TransClaw U-Net: Claw U-Net with Transformers for Medical Image Segmentation](https://mlsanity.com/paper/2107.05188/) (2021)
- [Calibrating Label Distribution for Class-Imbalanced Barely-Supervised Knee Segmentation](https://mlsanity.com/paper/2205.03644/) (2022)
- [Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2112.02508/) (2021)
- [3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers](https://mlsanity.com/paper/2310.07781/) (2023)
- [T-Mamba: A unified framework with Long-Range Dependency in dual-domain for 2D & 3D Tooth Segmentation](https://mlsanity.com/paper/2404.01065/) (2024)
- [CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal Segmentation in MRI](https://mlsanity.com/paper/2203.15163/) (2022)
- [CTooth: A Fully Annotated 3D Dataset and Benchmark for Tooth Volume Segmentation on Cone Beam Computed Tomography Images](https://mlsanity.com/paper/2206.08778/) (2022)
- [CTooth+: A Large-scale Dental Cone Beam Computed Tomography Dataset and Benchmark for Tooth Volume Segmentation](https://mlsanity.com/paper/2208.01643/) (2022)
- [Dual-Task Mutual Learning for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2103.04708/) (2021)
- [PMFSNet: Polarized Multi-scale Feature Self-attention Network For Lightweight Medical Image Segmentation](https://mlsanity.com/paper/2401.07579/) (2024)
- [ViTBIS: Vision Transformer for Biomedical Image Segmentation](https://mlsanity.com/paper/2201.05920/) (2022)
- [Competitive Ensembling Teacher-Student Framework for Semi-Supervised Left Atrium MRI Segmentation](https://mlsanity.com/paper/2310.13955/) (2023)
- [DeepDoseNet: A Deep Learning model for 3D Dose Prediction in Radiation Therapy](https://mlsanity.com/paper/2111.00077/) (2021)
- [Deep learning-based neurodevelopmental assessment in preterm infants](https://mlsanity.com/paper/2601.11944/) (2026)
- [Unpaired cross-modality educed distillation (CMEDL) for medical image segmentation](https://mlsanity.com/paper/2107.07985/) (2021)
- [MOSformer: Momentum encoder-based inter-slice fusion transformer for medical image segmentation](https://mlsanity.com/paper/2401.11856/) (2024)
- [REHRSeg: Unleashing the Power of Self-Supervised Super-Resolution for Resource-Efficient 3D MRI Segmentation](https://mlsanity.com/paper/2410.10097/) (2024)
- [The Application of Deep Learning for Lymph Node Segmentation: A Systematic Review](https://mlsanity.com/paper/2505.06118/) (2025)
- [Cardiac Segmentation on CT Images through Shape-Aware Contour Attentions](https://mlsanity.com/paper/2105.13153/) (2021)
- [HCDG: A Hierarchical Consistency Framework for Domain Generalization on Medical Image Segmentation](https://mlsanity.com/paper/2109.05742/) (2021)
- [Parameter Decoupling Strategy for Semi-supervised 3D Left Atrium Segmentation](https://mlsanity.com/paper/2109.09596/) (2021)
- [Memory-efficient Segmentation of High-resolution Volumetric MicroCT Images](https://mlsanity.com/paper/2205.15941/) (2022)
- [Interpretable Small Training Set Image Segmentation Network Originated from Multi-Grid Variational Model](https://mlsanity.com/paper/2306.14097/) (2023)
- [OARnet: Automated organs-at-risk delineation in Head and Neck CT images](https://mlsanity.com/paper/2108.13987/) (2021)
- [SECP-Net: SE-Connection Pyramid Network of Organ At Risk Segmentation for Nasopharyngeal Carcinoma](https://mlsanity.com/paper/2112.14026/) (2021)
- [Estimation of mitral valve hinge point coordinates -- deep neural net for echocardiogram segmentation](https://mlsanity.com/paper/2301.08782/) (2023)
- [Densely Decoded Networks with Adaptive Deep Supervision for Medical Image Segmentation](https://mlsanity.com/paper/2402.02649/) (2024)

## Abstract (arXiv)

Automatic and accurate whole-heart and great vessel segmentation from 3D cardiac magnetic resonance (MR) images plays an important role in the computer-assisted diagnosis and treatment of cardiovascular disease. However, this task is very challenging due to ambiguous cardiac borders and large anatomical variations among different subjects. In this paper, we propose a novel densely-connected volumetric convolutional neural network, referred as DenseVoxNet, to automatically segment the cardiac and vascular structures from 3D cardiac MR images. The DenseVoxNet adopts the 3D fully convolutional architecture for effective volume-to-volume prediction. From the learning perspective, our DenseVoxNet has three compelling advantages. First, it preserves the maximum information flow between layers by a densely-connected mechanism and hence eases the network training. Second, it avoids learning redundant feature maps by encouraging feature reuse and hence requires fewer parameters to achieve high performance, which is essential for medical applications with limited training data. Third, we add auxiliary side paths to strengthen the gradient propagation and stabilize the learning process. We demonstrate the effectiveness of DenseVoxNet by comparing it with the state-of-the-art approaches from HVSMR 2016 challenge in conjunction with MICCAI, and our network achieves the best dice coefficient. We also show that our network can achieve better performance than other 3D ConvNets but with fewer parameters.