Medical images used in clinical practice are heterogeneous and not the same quality as scans studied in academic research. Preprocessing breaks down in extreme cases when anatomy, artifacts, or imaging parameters are unusual or protocols are different. Methods robust to these variations are most needed. A novel deep learning method is proposed for fast and accurate segmentation of the human brain into 132 regions. The proposed model uses an efficient U-Net-like network and benefits from the intersection points of different views and hierarchical relations for the fusion of the orthogonal 2D planes and brain labels during the end-to-end training. Weakly supervised learning is deployed to take the advantage of partially labeled data for the whole brain segmentation and estimation of the intracranial volume (ICV). Moreover, data augmentation is used to expand the magnetic resonance imaging (MRI) data by generating realistic brain scans with high variability for robust training of the model while preserving data privacy. The proposed method can be applied to brain MRI data including skull or any other artifacts without preprocessing the images or a drop in performance. Several experiments using different atlases are conducted to evaluate the segmentation performance of the trained model compared to the state-of-the-art, and the results show higher segmentation accuracy and robustness of the proposed model compared to the existing methods across different intra- and inter-domain datasets.
翻译:临床实践中使用的医学图像多种多样,质量与学术研究研究的扫描不同。当解剖、人工制品或成像参数不同或程序不同时,在极端情况下,预处理会发生中断;最需要的是能够适应这些变异的方法。提出了一种新的深层次学习方法,将人类大脑快速和准确分解到132个区域。拟议的模型使用一个高效的U-Net类网络,并受益于不同观点和等级关系交叉点的不同观点和分级关系,以便在最后到最后训练期间将正正方的2D飞机和大脑标签混合在一起。运用了微弱的监督性学习,以利用部分贴标签的数据,用于整个大脑分解和估计内部体积(ICV)量。此外,还利用数据增强来扩大磁共振成像(MRI)数据,方法是产生现实的脑扫描和高度变异性,用于对模型进行稳健的培训,同时保护数据隐私。提议的方法可以适用于大脑MRI数据,包括头骨或任何其他人工制品,而不预先处理图像或性能下降。利用一些不同的受监督的实验,利用不同的分解式进行若干次实验,以评价不同的分解结果,比较现有的分解式分析结果,比较不同的分解结果,以显示不同的分解结果,比较不同的分解结果。