Department of Computer & Information Science, Norwegian University of Science & Technology, Sem Saelands vei 7-9, NO-7491 Trondheim, Norway.
Nuclear Medicine Department, Oncology Clinic 'ELPIDA', Children's Hospital 'A. Sofia', Goudi, Greece.
Comput Med Imaging Graph. 2018 Dec;70:83-100. doi: 10.1016/j.compmedimag.2018.10.002. Epub 2018 Oct 5.
Multiple sclerosis (MS) is a chronic disease. It affects the central nervous system and its clinical manifestation can variate. Magnetic Resonance Imaging (MRI) is often used to detect, characterize and quantify MS lesions in the brain, due to the detailed structural information that it can provide. Manual detection and measurement of MS lesions in MRI data is time-consuming, subjective and prone to errors. Therefore, multiple automated methodologies for MRI-based MS lesion segmentation have been proposed. Here, a review of the state-of-the-art of automatic methods available in the literature is presented. The current survey provides a categorization of the methodologies in existence in terms of their input data handling, their main strategy of segmentation and their type of supervision. The strengths and weaknesses of each category are analyzed and explicitly discussed. The positive and negative aspects of the methods are highlighted, pointing out the future trends and, thus, leading to possible promising directions for future research. In addition, a further clustering of the methods, based on the databases used for their evaluation, is provided. The aforementioned clustering achieves a reliable comparison among methods evaluated on the same databases. Despite the large number of methods that have emerged in the field, there is as yet no commonly accepted methodology that has been established in clinical practice. Future challenges such as the simultaneous exploitation of more sophisticated MRI protocols and the hybridization of the most promising methods are expected to further improve the performance of the segmentation.
多发性硬化症（MS）是一种慢性疾病。它影响中枢神经系统，其临床表现可能多种多样。磁共振成像（MRI）常用于检测、特征描述和量化脑部的 MS 病变，因为它可以提供详细的结构信息。在 MRI 数据中手动检测和测量 MS 病变既耗时又主观，且容易出错。因此，已经提出了多种基于 MRI 的 MS 病变分割的自动化方法。这里，我们对文献中现有的自动方法进行了综述。目前的调查根据输入数据处理、主要分割策略及其类型的监督，对现有的方法进行了分类。分析并明确讨论了每个类别的优缺点。突出了方法的优缺点，指出了未来的趋势，并为未来的研究提供了可能有前途的方向。此外，还根据用于评估的数据库对方法进行了进一步的聚类。上述聚类实现了在相同数据库上进行评估的方法之间的可靠比较。尽管该领域已经出现了大量的方法，但在临床实践中尚未建立普遍接受的方法。未来的挑战，如同时利用更复杂的 MRI 方案和杂交最有前途的方法，预计将进一步提高分割的性能。
