# Deep Learning for Medical Image Segmentation

arXiv: https://arxiv.org/abs/1505.02000 · HTML: https://mlsanity.com/paper/1505.02000/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2015.

## Built on by (most cited first, 11 of 11)

- [Neural ShDF: Reviving an Efficient and Consistent Mesh Segmentation Method](https://mlsanity.com/paper/2306.11737/) (2023)
- [Statistical Management of the False Discovery Rate in Medical Instance Segmentation Based on Conformal Risk Control](https://mlsanity.com/paper/2504.04482/) (2025)
- [Framework for lung CT image segmentation based on UNet++](https://mlsanity.com/paper/2501.02428/) (2025)
- [A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation](https://mlsanity.com/paper/2502.05396/) (2025)
- [ProSMA-UNet: Decoder Conditioning for Proximal-Sparse Skip Feature Selection](https://mlsanity.com/paper/2603.03187/) (2026)
- [Look at here : Utilizing supervision to attend subtle key regions](https://mlsanity.com/paper/2111.13233/) (2021)
- [Learning Multi-Modal Brain Tumor Segmentation from Privileged Semi-Paired MRI Images with Curriculum Disentanglement Learning](https://mlsanity.com/paper/2208.12781/) (2022)
- [Fourier Disentangled Multimodal Prior Knowledge Fusion for Red Nucleus Segmentation in Brain MRI](https://mlsanity.com/paper/2211.01353/) (2022)
- [Two Approaches to Supervised Image Segmentation](https://mlsanity.com/paper/2307.10123/) (2023)
- [3D Medical Imaging Segmentation on Non-Contrast CT](https://mlsanity.com/paper/2503.08361/) (2025)
- [Distributed Deep Learning for Medical Image Denoising with Data Obfuscation](https://mlsanity.com/paper/2511.06006/) (2025)

## Abstract (arXiv)

This report provides an overview of the current state of the art deep learning architectures and optimisation techniques, and uses the ADNI hippocampus MRI dataset as an example to compare the effectiveness and efficiency of different convolutional architectures on the task of patch-based 3-dimensional hippocampal segmentation, which is important in the diagnosis of Alzheimer's Disease. We found that a slightly unconventional "stacked 2D" approach provides much better classification performance than simple 2D patches without requiring significantly more computational power. We also examined the popular "tri-planar" approach used in some recently published studies, and found that it provides much better results than the 2D approaches, but also with a moderate increase in computational power requirement. Finally, we evaluated a full 3D convolutional architecture, and found that it provides marginally better results than the tri-planar approach, but at the cost of a very significant increase in computational power requirement.