Abstract
Thalamic alterations are relevant to many neurological disorders including Alzheimer's disease, Parkinson's disease and multiple sclerosis. Routine interventions to improve symptom severity in movement disorders, for example, often consist of surgery or deep brain stimulation to diencephalic nuclei. Therefore, accurate delineation of grey matter thalamic subregions is of the upmost clinical importance. MRI is highly appropriate for structural segmentation as it provides different views of the anatomy from a single scanning session. Though with several contrasts potentially available, it is also of increasing importance to develop new image segmentation techniques that can operate multi-spectrally. We hereby propose a new segmentation method for use with multi-modality data, which we evaluated for automated segmentation of major thalamic subnuclear groups using T1-, T2*-weighted and quantitative susceptibility mapping (QSM) information. The proposed method consists of four steps: highly iterative image co-registration, manual segmentation on the average training-data template, supervised learning for pattern recognition, and a final convex optimisation step imposing further spatial constraints to refine the solution. This led to solutions in greater agreement with manual segmentation than the standard Morel atlas based approach. Furthermore, we show that the multi-contrast approach boosts segmentation performances. We then investigated whether prior knowledge using the training-template contours could further improve convex segmentation accuracy and robustness, which led to highly precise multi-contrast segmentations in single subjects. This approach can be extended to most 3D imaging data types and any region of interest discernible in single scans or multi-subject templates.
Abstract (translated)
丘脑的改变与许多神经系统疾病有关，包括阿尔茨海默病，帕金森病和多发性硬化症。例如，常规干预措施可改善运动障碍中的症状严重程度，通常包括手术或对脑间质核的深部脑刺激。因此，准确描绘灰质丘脑子区域是最重要的临床重要性。 MRI非常适合于结构分割，因为它从单次扫描会话中提供了不同的解剖结构视图。尽管可能存在若干对比，但是开发可以多光谱操作的新图像分割技术也变得越来越重要。我们在此提出一种用于多模态数据的新分割方法，我们使用T1-，T2 * - 加权和定量磁敏度图（QSM）信息评估主要丘脑亚核组的自动分割。所提出的方法包括四个步骤：高度迭代图像共同配准，对平均训练数据模板的手动分割，用于模式识别的监督学习，以及施加进一步空间约束以细化解决方案的最终凸优化步骤。与标准的基于Morel图谱的方法相比，这导致解决方案与手动分割更加一致。此外，我们表明多对比度方法提高了分割性能。然后，我们研究了使用训练模板轮廓的先验知识是否可以进一步提高凸分割精度和鲁棒性，从而在单个主体中产生高度精确的多对比度分割。该方法可以扩展到大多数3D成像数据类型以及在单次扫描或多主题模板中可辨别的任何感兴趣区域。
URL
https://arxiv.org/abs/1807.10757
