CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia.
CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia.
Neuroimage. 2019 Nov 1;201:116018. doi: 10.1016/j.neuroimage.2019.116018. Epub 2019 Jul 15.
The deep grey matter (DGM) nuclei of the brain play a crucial role in learning, behaviour, cognition, movement and memory. Although automated segmentation strategies can provide insight into the impact of multiple neurological conditions affecting these structures, such as Multiple Sclerosis (MS), Huntington's disease (HD), Alzheimer's disease (AD), Parkinson's disease (PD) and Cerebral Palsy (CP), there are a number of technical challenges limiting an accurate automated segmentation of the DGM. Namely, the insufficient contrast of T1 sequences to completely identify the boundaries of these structures, as well as the presence of iso-intense white matter lesions or extensive tissue loss caused by brain injury. Therefore in this systematic review, 269 eligible studies were analysed and compared to determine the optimal approaches for addressing these technical challenges. The automated approaches used among the reviewed studies fall into three broad categories, atlas-based approaches focusing on the accurate alignment of atlas priors, algorithmic approaches which utilise intensity information to a greater extent, and learning-based approaches that require an annotated training set. Studies that utilise freely available software packages such as FIRST, FreeSurfer and LesionTOADS were also eligible, and their performance compared. Overall, deep learning approaches achieved the best overall performance, however these strategies are currently hampered by the lack of large-scale annotated data. Improving model generalisability to new datasets could be achieved in future studies with data augmentation and transfer learning. Multi-atlas approaches provided the second-best performance overall, and may be utilised to construct a "silver standard" annotated training set for deep learning. To address the technical challenges, providing robustness to injury can be improved by using multiple channels, highly elastic diffeomorphic transformations such as LDDMM, and by following atlas-based approaches with an intensity driven refinement of the segmentation, which has been done with the Expectation Maximisation (EM) and level sets methods. Accounting for potential lesions should be achieved with a separate lesion segmentation approach, as in LesionTOADS. Finally, to address the issue of limited contrast, R2*, T2* and QSM sequences could be used to better highlight the DGM due to its higher iron content. Future studies could look to additionally acquire these sequences by retaining the phase information from standard structural scans, or alternatively acquiring these sequences for only a training set, allowing models to learn the "improved" segmentation from T1-sequences alone.
大脑的深灰色物质（DGM）核对于学习、行为、认知、运动和记忆起着至关重要的作用。虽然自动化分割策略可以深入了解影响这些结构的多种神经疾病，如多发性硬化症（MS）、亨廷顿病（HD）、阿尔茨海默病（AD）、帕金森病（PD）和脑瘫（CP），但存在一些技术挑战限制了对 DGM 的准确自动化分割。即，T1 序列的对比度不足，无法完全识别这些结构的边界，以及存在同强度的白质病变或由脑损伤引起的广泛组织损失。因此，在本系统综述中，分析了 269 项符合条件的研究，并进行了比较，以确定解决这些技术挑战的最佳方法。综述中所采用的自动化方法可分为三大类，基于图谱的方法侧重于准确对齐图谱先验，算法方法更广泛地利用强度信息，以及基于学习的方法需要有注释的训练集。利用 FIRST、FreeSurfer 和 LesionTOADS 等免费软件包的研究也符合条件，并对其性能进行了比较。总的来说，深度学习方法的整体性能最佳，但这些策略目前受到缺乏大规模注释数据的限制。在未来的研究中，可以通过数据增强和迁移学习来提高模型对新数据集的泛化能力。多图谱方法的整体性能次之，可用于构建深度学习的“银标准”注释训练集。为了解决技术挑战，可以通过使用多通道、高度弹性的变形（如 LDDMM）和基于图谱的方法来提高对损伤的鲁棒性，并对分割进行基于强度的细化，这已经通过期望最大化（EM）和水平集方法来实现。可以使用单独的病变分割方法，如 LesionTOADS，来解决潜在病变的问题。最后，为了解决对比度有限的问题，可以使用 R2*、T2*和 QSM 序列，由于其铁含量较高，因此可以更好地突出 DGM。未来的研究可以通过保留标准结构扫描的相位信息来获取这些序列，或者仅为训练集获取这些序列，允许模型仅从 T1 序列学习“改进”的分割。
