本文为匈牙利布达佩斯理工大学（作者：Adaloglou M. Nikolaos）的硕士论文，共98页。

磁共振图像中的体分割对于诊断、监测和治疗计划是必需的。手工操作需要解剖学知识，成本高，耗时长，而且由于人为因素可能不准确。自动分割可以节省医生的时间，并为进一步分析提供精确的可重复的解决方案。

本文研究了多模态三维磁共振图像（MRI）的自动脑分割技术。对目前最先进的三维深度神经网络进行了广泛的比较分析。我们从描述MR成像的基本原理开始，因为理解输入数据对于训练深度结构至关重要。然后，我们通过广泛分析深度网络的每个组成部分（层），为读者提供深度学习工作原理的概述。在分别研究了磁共振和深度学习两个领域之后，我们试图以一个更广阔的视角来看待这两个领域的交叉，以及从MR图像重建到医学图像生成的不同应用范围。我们的工作集中在多模态脑分割上。在我们的实验中，使用了来自医学图像挑战的两个常见基准数据集。

脑MR分割挑战旨在通过提供一个3D-MRI数据集和医生注释的肿瘤分割标签来评估最新的脑分割方法。为了评估最先进的三维设计，我们简要分析了相关的方法，并提供给读者在设计选择背后的直觉。我们通过广泛的评估对基线架构进行比较分析。所实现的神经网络是基于原始论文的设计。最后，我们讨论了报告的结果，并为在PyTorch中实现开源医学分割库以及最常见的医学MRI数据集加载程序提供了未来发展的方向。目标是为医学深度成像任务生成3D深度学习库。我们坚信开放式的研究和可重复的学习。为了重现我们的结果，本论文的代码和材料可在以下网站下载https://github.com/black0017/MedicalZooPytorch

Volumetric segmentation in magnetic resonance images is mandatory for the diagnosis, monitoring, and treatment planning. Manual practices require anatomical knowledge, are expensive, time consuming and can be inaccurate due to human factor. Automated segmentation can save physicians time and provide an accurate reproducible solution for further analysis. In this thesis, automated brain segmentation from multi-modal 3D magnetic resonance images (MRIs) is studied. An extensive comparative analysis of state-of-the-art 3D deep neural networks for brain sub-region segmentation is performed. We start by describing the fundamentals of MR Imaging because it is crucial to understand your input data to train a deep architecture. Then, we provide the reader with an overview of how deep learning works by extensively analyzing every component (layer) of a deep network. After we study the fields of magnetic resonance and deep learning separately, we attempt give a broader perspective of the intersection of this two fields with a different range of application of deep networks, from MR image reconstruction to medical image generation. Our work is focused on multi-modal brain segmentation. For our experiments, we used two common benchmark datasets from medical image challenges. Brain MR segmentation challenges aim to evaluate state-of-the-art methods for the segmentation of brain by providing a 3D MRI dataset with ground truth tumor segmentation labels annotated by physicians. In order to evaluate state-of-the-art 3D architectures, we briefly analyze the author’s approaches, as well as to provide the reader with an intuition behind the design choices. We perform a comparative analysis of the baseline architectures through extensive evaluations. The implemented networks were based on the specifications of the original papers. Finally, we discuss the reported results and provide future directions for implementing an open-source medical segmentation library in PyTorch along with data loaders of the most common medical MRI datasets. The goal is to produce a 3D deep learning library for medical imaging related tasks. We strongly believe in open and reproducible deep learning research. In order to reproduce our results, the code (alpha release) and materials of this thesis are available in https://github.com/black0017/MedicalZooPytorch

1.       磁共振成像基础

2. 机器学习与深度学习原理

3. 现有的深度神经网络架构

4. 深度学习在医学成像与MRI的应用

5. 多模态脑MRI数据集、直觉和预处理

6. 实验评估

7. 结论与展望

附录A 医学影像学术语表

附录B MRI术语表

附录C 医学成像数据下载网址

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