# Dilated deeply supervised networks for hippocampus segmentation in MRI

arXiv: https://arxiv.org/abs/1903.09097 · HTML: https://mlsanity.com/paper/1903.09097/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2019.

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

- [A Comprehensive Review on Deep Supervision: Theories and Applications](https://mlsanity.com/paper/2207.02376/) (2022)
- [Continuous U-Net: Faster, Greater and Noiseless](https://mlsanity.com/paper/2302.00626/) (2023)
- [Feedback Chain Network For Hippocampus Segmentation](https://mlsanity.com/paper/2211.07891/) (2022)
- [CV-Attention UNet: Attention-based UNet for 3D Cerebrovascular Segmentation of Enhanced TOF-MRA Images](https://mlsanity.com/paper/2311.10224/) (2023)
- [Two-Steps Neural Networks for an Automated Cerebrovascular Landmark Detection](https://mlsanity.com/paper/2507.02349/) (2025)

## Abstract (arXiv)

Tissue loss in the hippocampi has been heavily correlated with the progression of Alzheimer's Disease (AD). The shape and structure of the hippocampus are important factors in terms of early AD diagnosis and prognosis by clinicians. However, manual segmentation of such subcortical structures in MR studies is a challenging and subjective task. In this paper, we investigate variants of the well known 3D U-Net, a type of convolution neural network (CNN) for semantic segmentation tasks. We propose an alternative form of the 3D U-Net, which uses dilated convolutions and deep supervision to incorporate multi-scale information into the model. The proposed method is evaluated on the task of hippocampus head and body segmentation in an MRI dataset, provided as part of the MICCAI 2018 segmentation decathlon challenge. The experimental results show that our approach outperforms other conventional methods in terms of different segmentation accuracy metrics.