用AI分析肺纤维化患者气道影像，预测生存期。
Predicting Patient Survival with Airway Biomarkers using nn-Unet/Radiomics
- 三阶段流程：分割气道→提取影像特征→分类预测
- 气道结构与气管区域特征对生存预测有显著价值
- 适合医学影像分析、呼吸系统疾病研究者参考
AIIB 2023竞赛旨在评估气道相关影像生物标志物在预测肺纤维化患者生存结局中的作用。本研究提出一个三阶段综合方法：首先使用nn-Unet分割网络精准勾画气道结构边界；随后从气管中心及气道包围盒区域提取放射组学特征，以挖掘潜在的生存相关信号；最后将提取的特征输入SVM分类器进行预测。在任务1（分割）中获得0.8601的整体评分，在任务2（分类）中达到0.7346的评分，表明该方法在气道形态分析与生存预测方面具有较高效能。
原文摘要 · Abstract (English)
The primary objective of the AIIB 2023 competition is to evaluate the predictive significance of airway-related imaging biomarkers in determining the survival outcomes of patients with lung fibrosis.This study introduces a comprehensive three-stage approach. Initially, a segmentation network, namely nn-Unet, is employed to delineate the airway's structural boundaries. Subsequently, key features are extracted from the radiomic images centered around the trachea and an enclosing bounding box around the airway. This step is motivated by the potential presence of critical survival-related insights within the tracheal region as well as pertinent information encoded in the structure and dimensions of the airway. Lastly, radiomic features obtained from the segmented areas are integrated into an SVM classifier. We could obtain an overall-score of 0.8601 for the segmentation in Task 1 while 0.7346 for the classification in Task 2.
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