Chen Bang-Chuan, Shen Chung-Yi, Chai Jyh-Wen, Hwang Ren-Hung, Chiang Wei-Chuan, Chou Chi-Hsiang, Liu Wei-Min
The Department of Neurological Institute, Taichung Veterans General Hospital, Taichung, Taiwan, ROC.
Department of Computer Science and Information Engineering and Advanced Institute of Manufacturing with High-tech Innovations, National Chung Cheng University, Chiayi, Taiwan, ROC.
PLoS One. 2025 Jun 11;20(6):e0323692. doi: 10.1371/journal.pone.0323692. eCollection 2025.
Non-contrast Computed Tomography (NCCT) quickly diagnoses acute cerebral hemorrhage or infarction. However, Deep-Learning (DL) algorithms often generate false alarms (FA) beyond the cerebral region.
We introduce an enhanced brain tissue segmentation method for infarction lesion segmentation (ILS). This method integrates an adaptive result fusion strategy to confine the search operation within cerebral tissue, effectively reducing FAs. By leveraging fused brain masks, DL-based ILS algorithms focus on pertinent radiomic correlations. Various U-Net models underwent rigorous training, with exploration of diverse fusion strategies. Further refinement entailed applying a 9x9 Gaussian filter with unit standard deviation followed by binarization to mitigate false positives. Performance evaluation utilized Intersection over Union (IoU) and Hausdorff Distance (HD) metrics, complemented by external validation on a subset of the COCO dataset.
Our study comprised 20 ischemic stroke patients (14 males, 4 females) with an average age of 68.9 ± 11.7 years. Fusion with UNet2+ and UNet3 + yielded an IoU of 0.955 and an HD of 1.33, while fusion with U-net, UNet2 + , and UNet3 + resulted in an IoU of 0.952 and an HD of 1.61. Evaluation on the COCO dataset demonstrated an IoU of 0.463 and an HD of 584.1 for fusion with UNet2+ and UNet3 + , and an IoU of 0.453 and an HD of 728.0 for fusion with U-net, UNet2 + , and UNet3 + .
Our adaptive fusion strategy significantly diminishes FAs and enhances the training efficacy of DL-based ILS algorithms, surpassing individual U-Net models. This methodology holds promise as a versatile, data-independent approach for cerebral lesion segmentation.
非增强计算机断层扫描（NCCT）可快速诊断急性脑出血或梗死。然而，深度学习（DL）算法经常在脑区之外产生误报（FA）。
我们引入一种用于梗死病变分割（ILS）的增强型脑组织分割方法。该方法集成了自适应结果融合策略，将搜索操作限制在脑组织内，有效减少误报。通过利用融合的脑掩码，基于DL的ILS算法专注于相关的放射组学相关性。对各种U-Net模型进行了严格训练，并探索了不同的融合策略。进一步优化包括应用标准差为1的9x9高斯滤波器，然后进行二值化以减轻假阳性。性能评估使用交并比（IoU）和豪斯多夫距离（HD）指标，并在COCO数据集的一个子集上进行外部验证。
我们的研究包括20例缺血性中风患者（14例男性，4例女性），平均年龄为68.9±11.7岁。与UNet2+和UNet3+融合产生的IoU为0.955，HD为1.33，而与U-net、UNet2+和UNet3+融合产生的IoU为0.952，HD为1.61。在COCO数据集上的评估表明，与UNet2+和UNet3+融合的IoU为0.463，HD为584.1，与U-net、UNet2+和UNet3+融合的IoU为0.453，HD为728.0。
我们的自适应融合策略显著减少了误报，提高了基于DL的ILS算法的训练效果，优于单个U-Net模型。这种方法有望成为一种通用的、与数据无关的脑病变分割方法。
