DeepHIPS: A novel Deep Learning based Hippocampus Subfield Segmentation method

TL;DR

This paper introduces DeepHIPS, a deep learning-based pipeline for automatic hippocampus subfield segmentation, improving accuracy and speed over existing methods, aiding early detection of neurodegenerative diseases.

Contribution

The paper presents a novel deeply supervised convolutional neural network for hippocampus subfield segmentation, outperforming state-of-the-art methods in accuracy and efficiency.

Findings

Improved segmentation accuracy over existing methods

Faster execution time compared to prior approaches

Validated on two hippocampus delineation protocols

Abstract

The automatic assessment of hippocampus volume is an important tool in the study of several neurodegenerative diseases such as Alzheimer's disease. Specifically, the measurement of hippocampus subfields properties is of great interest since it can show earlier pathological changes in the brain. However, segmentation of these subfields is very difficult due to their complex structure and for the need of high-resolution magnetic resonance images manually labeled. In this work, we present a novel pipeline for automatic hippocampus subfield segmentation based on a deeply supervised convolutional neural network. Results of the proposed method are shown for two available hippocampus subfield delineation protocols. The method has been compared to other state-of-the-art methods showing improved results in terms of accuracy and execution time.