School of Mathematics, Physics, and Information Science, Shaoxing University, 900 ChengNan Rd, Shaoxing, 312000, Zhejiang, China.
Med Biol Eng Comput. 2023 Sep;61(9):2329-2339. doi: 10.1007/s11517-023-02836-9. Epub 2023 Apr 17.
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.
从磁共振（MR）脑图像中准确分割出海马体是研究脑疾病的关键步骤。然而，由于海马体图像的信号对比度低、形状不规则和结构尺寸小，因此这项任务具有挑战性。近年来，已经提出了几种用于海马体分割的深度卷积网络，这些方法已经达到了最先进的性能。这些方法通常使用大的图像补丁来训练网络，因为更大的补丁有助于捕获长程上下文信息。然而，这种方法增加了计算负担，忽略了边界区域的重要性。在这项研究中，我们提出了一种基于深度学习的具有边界细化的海马体分割方法。我们的方法涉及两个主要步骤。首先，我们提出了一个卷积网络，该网络以大的图像补丁作为输入进行初步分割。然后，我们在海马体边界周围提取小的图像补丁，用于训练第二个卷积神经网络，该网络细化边界区域的分割。我们在一个公开可用的数据集上验证了我们提出的方法，并证明它显著提高了使用单一尺寸图像补丁作为输入的卷积神经网络的性能。总之，我们的研究提出了一种新的海马体分割方法，该方法改进了当前最先进的方法。通过引入边界细化步骤，我们的方法在海马体分割中实现了更高的准确性，可能有助于脑疾病的研究。
