用AI自动分割脑梗死影像，提升诊疗效率与预后判断
Automated Segmentation of Ischemic Stroke Lesions in Non-Contrast Computed Tomography Images for Enhanced Treatment and Prognosis
- 基于nnU-Net框架实现非增强CT上脑梗病灶的自动分割
- 调整异常值后分割精度达Dice 0.752，IoU 0.643
- 适合放射科医生和神经科临床决策辅助使用
中风是全球第二大死亡原因，且在低收入和中等收入国家日益普遍。及时干预可显著影响中风患者存活率和治疗后生活质量。然而，确认中风及其亚型的标准且最广泛可用的影像方法——非增强计算机断层扫描（NCCT），在缺血性中风病例中更难、耗时更长。为此，我们基于nnU-Net框架开发了一种自动化方法，用于在NCCT图像中分割缺血性中风病灶，旨在提升早期治疗效率并改善预后。在采样数据集上，该方法获得0.596的Dice分数和0.501的交并比（IoU）；剔除异常值后，得分分别提升至0.752和0.643。准确勾画梗死区域有助于临床医生更好地评估梗死影响范围，并指导治疗方案。
原文摘要 · Abstract (English)
Stroke is the second leading cause of death worldwide, and is increasingly prevalent in low- and middle-income countries (LMICs). Timely interventions can significantly influence stroke survivability and the quality of life after treatment. However, the standard and most widely available imaging method for confirming strokes and their sub-types, the NCCT, is more challenging and time-consuming to employ in cases of ischemic stroke. For this reason, we developed an automated method for ischemic stroke lesion segmentation in NCCTs using the nnU-Net frame work, aimed at enhancing early treatment and improving the prognosis of ischemic stroke patients. We achieved Dice scores of 0.596 and Intersection over Union (IoU) scores of 0.501 on the sampled dataset. After adjusting for outliers, these scores improved to 0.752 for the Dice score and 0.643 for the IoU. Proper delineation of the region of infarction can help clinicians better assess the potential impact of the infarction, and guide treatment procedures.
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