Kamel Peter, Kanhere Adway, Kulkarni Pranav, Khalid Mazhar, Steger Rachel, Bodanapally Uttam, Gandhi Dheeraj, Parekh Vishwa, Yi Paul H
Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland School of Medicine, Baltimore, MD, USA.
J Imaging Inform Med. 2025 Apr;38(2):717-726. doi: 10.1007/s10278-024-00994-2. Epub 2024 Aug 13.
Segmentation of infarcts is clinically important in ischemic stroke management and prognostication. It is unclear what role the combination of DWI, ADC, and FLAIR MRI sequences provide for deep learning in infarct segmentation. Recent technologies in model self-configuration have promised greater performance and generalizability through automated optimization. We assessed the utility of DWI, ADC, and FLAIR sequences on ischemic stroke segmentation, compared self-configuring nnU-Net models to conventional U-Net models without manual optimization, and evaluated the generalizability of results on an external clinical dataset. 3D self-configuring nnU-Net models and standard 3D U-Net models with MONAI were trained on 200 infarcts using DWI, ADC, and FLAIR sequences separately and in all combinations. Segmentation results were compared between models using paired t-test comparison on a hold-out test set of 50 cases. The highest performing model was externally validated on a clinical dataset of 50 MRIs. nnU-Net with DWI sequences attained a Dice score of 0.810 ± 0.155. There was no statistically significant difference when DWI sequences were supplemented with ADC and FLAIR images (Dice score of 0.813 ± 0.150; p = 0.15). nnU-Net models significantly outperformed standard U-Net models for all sequence combinations (p < 0.001). On the external dataset, Dice scores measured 0.704 ± 0.199 for positive cases with false positives with intracranial hemorrhage. Highly optimized neural networks such as nnU-Net provide excellent stroke segmentation even when only provided DWI images, without significant improvement from other sequences. This differs from-and significantly outperforms-standard U-Net architectures. Results translated well to the external clinical environment and provide the groundwork for optimized acute stroke segmentation on MRI.
梗死灶分割在缺血性脑卒中的管理和预后评估中具有重要的临床意义。目前尚不清楚弥散加权成像（DWI）、表观扩散系数（ADC）和液体衰减反转恢复序列（FLAIR）的联合使用在梗死灶分割的深度学习中能发挥何种作用。模型自配置的最新技术有望通过自动优化实现更高的性能和通用性。我们评估了DWI、ADC和FLAIR序列在缺血性脑卒中分割中的效用，将自配置的nnU-Net模型与未经手动优化的传统U-Net模型进行比较，并在外部临床数据集上评估结果的通用性。使用DWI、ADC和FLAIR序列单独及组合对200个梗死灶训练了3D自配置nnU-Net模型和带有MONAI的标准3D U-Net模型。在50例病例的保留测试集上，使用配对t检验比较模型之间的分割结果。在50例MRI的临床数据集上对表现最佳的模型进行外部验证。使用DWI序列的nnU-Net的Dice评分为0.810±0.155。当DWI序列补充ADC和FLAIR图像时，差异无统计学意义（Dice评分为0.813±0.150；p = 0.15）。对于所有序列组合，nnU-Net模型显著优于标准U-Net模型（p < 0.001）。在外部数据集上，颅内出血假阳性的阳性病例的Dice评分为0.704±0.199。即使仅提供DWI图像，高度优化的神经网络如nnU-Net也能提供出色的脑卒中分割，其他序列并未带来显著改善。这与标准U-Net架构不同，且显著优于标准U-Net架构。结果在外部临床环境中具有良好的可重复性，为MRI上优化急性脑卒中分割奠定了基础。
