Department of Nuclear Medicine, Ghent University Hospital, Ghent, Belgium; Medical Imaging and Signal Processing (MEDISIP), Department of Electronics and Information Systems, Ghent University, Ghent, Belgium.
Department of Nuclear Medicine, Ghent University Hospital, Ghent, Belgium.
Comput Biol Med. 2018 Jul 1;98:39-47. doi: 10.1016/j.compbiomed.2018.05.005. Epub 2018 May 7.
Brain tumour segmentation in medical images is a very challenging task due to the large variety in tumour shape, position, appearance, scanning modalities and scanning parameters. Most existing segmentation algorithms use information from four different MRI-sequences, but since this is often not available, there is need for a method able to delineate the different tumour tissues based on a minimal amount of data. We present a novel approach using a Random Forests model combining voxelwise texture and abnormality features on a contrast-enhanced T1 and FLAIR MRI. We transform the two scans into 275 feature maps. A random forest model next calculates the probability to belong to 4 tumour classes or 5 normal classes. Afterwards, a dedicated voxel clustering algorithm provides the final tumour segmentation. We trained our method on the BraTS 2013 database and validated it on the larger BraTS 2017 dataset. We achieve median Dice scores of 40.9% (low-grade glioma) and 75.0% (high-grade glioma) to delineate the active tumour, and 68.4%/80.1% for the total abnormal region including edema. Our fully automated brain tumour segmentation algorithm is able to delineate contrast enhancing tissue and oedema with high accuracy based only on post-contrast T1-weighted and FLAIR MRI, whereas for non-enhancing tumour tissue and necrosis only moderate results are obtained. This makes the method especially suitable for high-grade glioma.
医学图像中的脑肿瘤分割是一项极具挑战性的任务，因为肿瘤的形状、位置、外观、扫描方式和扫描参数存在很大差异。大多数现有的分割算法都使用来自四种不同 MRI 序列的信息，但由于这些信息通常不可用，因此需要一种能够基于最少的数据来描绘不同肿瘤组织的方法。我们提出了一种新的方法，该方法使用随机森林模型，结合对比度增强 T1 和 FLAIR MRI 上的体素纹理和异常特征。我们将这两个扫描转换为 275 个特征图。然后，随机森林模型计算属于 4 个肿瘤类别或 5 个正常类别的概率。之后，专门的体素聚类算法提供最终的肿瘤分割。我们在 BraTS 2013 数据库上训练了我们的方法，并在更大的 BraTS 2017 数据集上进行了验证。我们的方法在勾画活跃肿瘤方面的平均 Dice 分数为 40.9%（低级别胶质瘤）和 75.0%（高级别胶质瘤），勾画总的异常区域（包括水肿）的平均 Dice 分数为 68.4%/80.1%。我们的全自动脑肿瘤分割算法仅基于增强后 T1 加权和 FLAIR MRI 即可非常准确地描绘增强组织和水肿，而对于非增强肿瘤组织和坏死，仅能获得中等结果。这使得该方法特别适用于高级别胶质瘤。
