# 3D Densely Convolutional Networks for Volumetric Segmentation

arXiv: https://arxiv.org/abs/1709.03199 · HTML: https://mlsanity.com/paper/1709.03199/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2017.

## Built on by (most cited first, 9 of 9)

- [Medical Image Segmentation with 3D Convolutional Neural Networks: A Survey](https://mlsanity.com/paper/2108.08467/) (2021)
- [CTooth+: A Large-scale Dental Cone Beam Computed Tomography Dataset and Benchmark for Tooth Volume Segmentation](https://mlsanity.com/paper/2208.01643/) (2022)
- [Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI](https://mlsanity.com/paper/2302.11571/) (2023)
- [JAS-GAN: Generative Adversarial Network Based Joint Atrium and Scar Segmentations on Unbalanced Atrial Targets](https://mlsanity.com/paper/2105.00234/) (2021)
- [Generalized Facial Manipulation Detection with Edge Region Feature Extraction](https://mlsanity.com/paper/2102.01381/) (2021)
- [DeepDoseNet: A Deep Learning model for 3D Dose Prediction in Radiation Therapy](https://mlsanity.com/paper/2111.00077/) (2021)
- [Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation](https://mlsanity.com/paper/2302.03296/) (2023)
- [OARnet: Automated organs-at-risk delineation in Head and Neck CT images](https://mlsanity.com/paper/2108.13987/) (2021)
- [Rewiring Development in Brain Segmentation: Leveraging Adult Brain Priors for Enhancing Infant MRI Segmentation](https://mlsanity.com/paper/2510.09306/) (2025)

## Abstract (arXiv)

In the isointense stage, the accurate volumetric image segmentation is a challenging task due to the low contrast between tissues. In this paper, we propose a novel very deep network architecture based on a densely convolutional network for volumetric brain segmentation. The proposed network architecture provides a dense connection between layers that aims to improve the information flow in the network. By concatenating features map of fine and coarse dense blocks, it allows capturing multi-scale contextual information. Experimental results demonstrate significant advantages of the proposed method over existing methods, in terms of both segmentation accuracy and parameter efficiency in MICCAI grand challenge on 6-month infant brain MRI segmentation.