Stroke Trials Unit, Division of Clinical Neuroscience, University of Nottingham, UK; Radiological Sciences, Division of Clinical Neuroscience, University of Nottingham, UK.
Stroke Trials Unit, Division of Clinical Neuroscience, University of Nottingham, UK; Department of Medicine, National University of Malaysia, Malaysia.
Comput Biol Med. 2019 Mar;106:126-139. doi: 10.1016/j.compbiomed.2019.01.022. Epub 2019 Jan 29.
Spontaneous intracerebral haemorrhage (SICH) is a common condition with high morbidity and mortality. Segmentation of haematoma and perihaematoma oedema on medical images provides quantitative outcome measures for clinical trials and may provide important markers of prognosis in people with SICH.
We take advantage of improved contrast seen on magnetic resonance (MR) images of patients with acute and early subacute SICH and introduce an automated algorithm for haematoma and oedema segmentation from these images. To our knowledge, there is no previously proposed segmentation technique for SICH that utilises MR images directly. The method is based on shape and intensity analysis for haematoma segmentation and voxel-wise dynamic thresholding of hyper-intensities for oedema segmentation.
Using Dice scores to measure segmentation overlaps between labellings yielded by the proposed algorithm and five different expert raters on 18 patients, we observe that our technique achieves overlap scores that are very similar to those obtained by pairwise expert rater comparison. A further comparison between the proposed method and a state-of-the-art Deep Learning segmentation on a separate set of 32 manually annotated subjects confirms the proposed method can achieve comparable results with very mild computational burden and in a completely training-free and unsupervised way.
Our technique can be a computationally light and effective way to automatically delineate haematoma and oedema extent directly from MR images. Thus, with increasing use of MR images clinically after intracerebral haemorrhage this technique has the potential to inform clinical practice in the future.
自发性脑出血（SICH）是一种发病率和死亡率都很高的常见疾病。在医学图像上对血肿和血肿周围水肿进行分割，可为临床试验提供定量的结果测量指标，并可能为 SICH 患者的预后提供重要的标志物。
我们利用磁共振（MR）图像上急性和早期亚急性 SICH 患者对比度的提高，并引入一种自动算法，从这些图像中对血肿和水肿进行分割。据我们所知，目前还没有利用 MR 图像直接对 SICH 进行分割的技术。该方法基于形状和强度分析进行血肿分割，以及对高信号进行基于体素的动态阈值分割以进行水肿分割。
使用 Dice 评分来衡量算法标签与 18 名患者的 5 位不同专家评估者之间的分割重叠度，我们发现我们的技术获得的重叠分数与专家两两评估之间的得分非常相似。在另一组 32 个手动标注的对象上，对我们的方法和一种最先进的深度学习分割方法进行进一步比较，证实了我们的方法可以在非常低的计算负担下，以完全无监督和无需训练的方式，获得可与深度学习分割方法相媲美的结果。
我们的技术可以成为一种计算简便且有效的方法，可直接从 MR 图像自动描绘血肿和水肿的范围。因此，随着 SICH 后 MR 图像在临床上的应用越来越广泛，该技术有可能在未来为临床实践提供信息。
