文献类型：期刊文献

英文题名：Deep convolutional neural network for hippocampus segmentation with boundary region refinement

作者：He, Guanghua[1];Zhang, Guying[1];Zhou, Lianlian[1];Zhu, Hancan[1]

机构：[1]Shaoxing Univ, Sch Math Phys & Informat Sci, 900 ChengNan Rd, Shaoxing 312000, Zhejiang, Peoples R China

年份：2023

外文期刊名：MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING

收录：SCI-EXPANDED(收录号：WOS:000973546400001)、、EI(收录号：20231713950171)、Scopus(收录号：2-s2.0-85153109785)、WOS

基金：This work was supported by the Scientific Research Project of Shaoxing University (20210038), Zhejiang Provincial Natural Science Foundation of China (LY19F020013), and National Natural Science Foundation of China (61602307, 61877039).; Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics.

语种：英文

外文关键词：Hippocampus segmentation; U-Net; Deep learning; Boundary refinement

外文摘要：Accurately segmenting the hippocampus from magnetic resonance (MR) brain images is a crucial step in studying brain disorders. However, this task is challenging due to the low signal contrast of hippocampal images, the irregular shape, and small structural size of the hippocampi. In recent years, several deep convolutional networks have been proposed for hippocampus segmentation, which have achieved state-of-the-art performance. These methods typically use large image patches for training the network, as larger patches are beneficial for capturing long- range contextual information. However, this approach increases the computational burden and overlooks the significance of the boundary region. In this study, we propose a deep learning-based method for hippocampus segmentation with boundary region refinement. Our method involves two main steps. First, we propose a convolutional network that takes large image patches as input for initial segmentation. Then, we extract small image patches around the hippocampal boundary for training the second convolutional neural network, which refines the segmentation in the boundary regions. We validate our proposed method on a publicly available dataset and demonstrate that it significantly improves the performance of convolutional neural networks that use single-size image patches as input. In conclusion, our study proposes a novel method for hippocampus segmentation, which improves upon the current state-of-the-art methods. By incorporating a boundary refinement step, our approach achieves higher accuracy in hippocampus segmentation and may facilitate research on brain disorders.