Li Chenjun, Yang Dian, Yao Shun, Wang Shuyue, Wu Ye, Zhang Le, Li Qiannuo, Cho Kang Ik Kevin, Seitz-Holland Johanna, Ning Lipeng, Legarreta Jon Haitz, Rathi Yogesh, Westin Carl-Fredrik, O'Donnell Lauren J, Sochen Nir A, Pasternak Ofer, Zhang Fan
University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
Comput Med Imaging Graph. 2025 Mar;120:102489. doi: 10.1016/j.compmedimag.2024.102489. Epub 2025 Jan 4.
In this study, we developed an Evidential Ensemble Neural Network based on Deep learning and Diffusion MRI, namely DDEvENet, for anatomical brain parcellation. The key innovation of DDEvENet is the design of an evidential deep learning framework to quantify predictive uncertainty at each voxel during a single inference. To do so, we design an evidence-based ensemble learning framework for uncertainty-aware parcellation to leverage the multiple dMRI parameters derived from diffusion MRI. Using DDEvENet, we obtained accurate parcellation and uncertainty estimates across different datasets from healthy and clinical populations and with different imaging acquisitions. The overall network includes five parallel subnetworks, where each is dedicated to learning the FreeSurfer parcellation for a certain diffusion MRI parameter. An evidence-based ensemble methodology is then proposed to fuse the individual outputs. We perform experimental evaluations on large-scale datasets from multiple imaging sources, including high-quality diffusion MRI data from healthy adults and clinically diffusion MRI data from participants with various brain diseases (schizophrenia, bipolar disorder, attention-deficit/hyperactivity disorder, Parkinson's disease, cerebral small vessel disease, and neurosurgical patients with brain tumors). Compared to several state-of-the-art methods, our experimental results demonstrate highly improved parcellation accuracy across the multiple testing datasets despite the differences in dMRI acquisition protocols and health conditions. Furthermore, thanks to the uncertainty estimation, our DDEvENet approach demonstrates a good ability to detect abnormal brain regions in patients with lesions that are consistent with expert-drawn results, enhancing the interpretability and reliability of the segmentation results.
在本研究中，我们基于深度学习和扩散磁共振成像（Diffusion MRI）开发了一种基于证据集成的神经网络，即DDEvENet，用于大脑解剖学分区。DDEvENet的关键创新在于设计了一个证据深度学习框架，以在单次推理过程中量化每个体素的预测不确定性。为此，我们设计了一个基于证据的集成学习框架，用于不确定性感知分区，以利用从扩散磁共振成像中导出的多个磁共振成像参数。使用DDEvENet，我们在来自健康和临床人群的不同数据集以及不同成像采集条件下获得了准确的分区和不确定性估计。整个网络包括五个并行子网络，每个子网络专门用于学习特定扩散磁共振成像参数的FreeSurfer分区。然后提出一种基于证据的集成方法来融合各个输出。我们对来自多个成像源的大规模数据集进行了实验评估，包括来自健康成年人的高质量扩散磁共振成像数据以及来自患有各种脑部疾病（精神分裂症、双相情感障碍、注意力缺陷多动障碍、帕金森病、脑小血管疾病以及患有脑肿瘤的神经外科患者）的参与者的临床扩散磁共振成像数据。与几种先进方法相比，我们的实验结果表明，尽管磁共振成像采集协议和健康状况存在差异，但在多个测试数据集上的分区准确性有了显著提高。此外，由于不确定性估计，我们的DDEvENet方法在检测病变患者异常脑区方面表现出良好能力，与专家绘制的结果一致，增强了分割结果的可解释性和可靠性。
