# Hippocampus segmentation in magnetic resonance images of Alzheimer's patients using Deep machine learning

arXiv: https://arxiv.org/abs/2106.06743 · HTML: https://mlsanity.com/paper/2106.06743/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 30 references that are in the map.
- First posted on arXiv 12 Jun 2021.

## Builds on (most cited first, 2 of 2)

- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [An Optimized PatchMatch for Multi-scale and Multi-feature Label Fusion](https://mlsanity.com/paper/1903.07165/) (2019)

## Abstract (arXiv)

Background: Alzheimers disease is a progressive neurodegenerative disorder and the main cause of dementia in aging. Hippocampus is prone to changes in the early stages of Alzheimers disease. Detection and observation of the hippocampus changes using magnetic resonance imaging (MRI) before the onset of Alzheimers disease leads to the faster preventive and therapeutic measures. Objective: The aim of this study was the segmentation of the hippocampus in magnetic resonance (MR) images of Alzheimers patients using deep machine learning method. Methods: U-Net architecture of convolutional neural network was proposed to segment the hippocampus in the real MRI data. The MR images of the 100 and 35 patients available in Alzheimers disease Neuroimaging Initiative (ADNI) dataset, was used for the train and test of the model, respectively. The performance of the proposed method was compared with manual segmentation by measuring the similarity metrics. Results: The desired segmentation achieved after 10 iterations. A Dice similarity coefficient (DSC) = 92.3%, sensitivity = 96.5%, positive predicted value (PPV) = 90.4%, and Intersection over Union (IoU) value for the train 92.94 and test 92.93 sets were obtained which are acceptable. Conclusion: The proposed approach is promising and can be extended in the prognosis of Alzheimers disease by the prediction of the hippocampus volume changes in the early stage of the disease.