论文标题
深度：一种基于深度学习的新型海马子场分割方法
DeepHIPS: A novel Deep Learning based Hippocampus Subfield Segmentation method
论文作者
论文摘要
海马体积的自动评估是研究多种神经退行性疾病（例如阿尔茨海默氏病）的重要工具。具体而言，海马子场特性的测量引起了人们的极大关注，因为它可以显示出大脑早期的病理变化。但是，由于它们的复杂结构以及需要手动标记的高分辨率磁共振图像，因此对这些子场的分割非常困难。在这项工作中，我们提出了基于深入监督的卷积神经网络的自动海马子场分割的新型管道。为两个可用的海马子场描述方案显示了所提出的方法的结果。该方法已与其他最先进的方法进行了比较，该方法在准确性和执行时间方面显示了改善的结果。
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.
