论文标题
使用2.5D修改效率的计算机断层扫描中气道分割的开源工具：对ATM22挑战的贡献
Open-source tool for Airway Segmentation in Computed Tomography using 2.5D Modified EfficientDet: Contribution to the ATM22 Challenge
论文作者
论文摘要
计算机断层扫描图像中的气道分割可用于分析肺部疾病，但是，手动分割是劳动力密集的，并且依赖于专家知识。本手稿详细介绍了我们对Miccai的2022 Airway Tree建模挑战的贡献，这是一项全自动方法进行气道细分的竞争。我们采用了先前开发的深度学习体系结构，该体系结构基于修改后的有效DET（MEDSEG），从头开始培训使用所提供的注释来对二元气道细分进行培训。我们的方法在内部验证中达到了90.72骰子，在外部验证上进行了95.52个骰子，在最终测试阶段进行了93.49个骰子，而不是专门设计或调整用于气道分割的。开源代码和PIP包用于使用我们的模型和训练的权重进行预测，请在https://github.com/miclab-unicamp/medseg中。
Airway segmentation in computed tomography images can be used to analyze pulmonary diseases, however, manual segmentation is labor intensive and relies on expert knowledge. This manuscript details our contribution to MICCAI's 2022 Airway Tree Modelling challenge, a competition of fully automated methods for airway segmentation. We employed a previously developed deep learning architecture based on a modified EfficientDet (MEDSeg), training from scratch for binary airway segmentation using the provided annotations. Our method achieved 90.72 Dice in internal validation, 95.52 Dice on external validation, and 93.49 Dice in the final test phase, while not being specifically designed or tuned for airway segmentation. Open source code and a pip package for predictions with our model and trained weights are in https://github.com/MICLab-Unicamp/medseg.
