分割
计算机科学
人工智能
任务(项目管理)
卷积神经网络
海马体
卷积(计算机科学)
模式识别(心理学)
深度学习
人工神经网络
机器学习
神经科学
心理学
经济
管理
作者
Lukas Folle,Sulaiman Vesal,Nishant Ravikumar,Andreas Maier
出处
期刊:Cornell University - arXiv
日期:2019-03-20
摘要
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.
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