Deep-Masker: A Deep Learning-based Tool to Assess Chord Length from Murine Lung Images.
Deep-Masker: A Deep Learning-based Tool to Assess Chord Length from Murine Lung Images.
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Deep-Masker：一种基于深度学习的工具，用于评估小鼠肺部图像的弦长。
DOI：
10.1165/rcmb.2023-0051ma
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发表时间：
2023
影响因子：
6.4
通讯作者：
Chandra,Divay
中科院分区：
文献类型：
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作者：
Pu,Jiantao;Leme,AdrianaS;deLimaESilva,Camilla;Beeche,Cameron;Nyunoya,Toru;Königshoff,Melanie;Chandra,Divay
在慢性阻塞性肺疾病（COPD）动物模型中，弦长是肺泡大小的间接测量值和关键终点。在评估弦长时，通过各种方法（包括手动掩蔽）将非肺泡结构的管腔从测量中排除。然而，手动掩蔽是资源密集型的，并且可以引入可变性和偏差。我们创建了一个完全自动化的基于深度学习的工具来掩蔽小鼠肺部图像并评估弦长，以促进COPD中的机制和治疗发现，称为Deep-Masker（可在http：//47.93.0.75：8110/login上获得）。我们使用来自12个品系的137只小鼠的1,217张图像训练了Deep-Masker的深度学习算法，这些小鼠暴露在室内空气或香烟烟雾中6个月。我们验证了该算法对手动掩蔽。Deep-Masker表现出很高的准确性，与手动掩蔽相比，暴露于室内空气的小鼠和暴露于香烟烟雾的小鼠的平均弦长差异分别为-0.3 ± 1.4%（rs = 0.99）和0.7 ± 1.9%（rs = 0.99）。深度掩蔽和手动掩蔽图像之间因香烟烟雾暴露导致的弦长变化的差异为6.0 ± 9.2%（rs = 0.95）。这些值超过了已发表的人工掩蔽（rs = 0.65）的观察者间变异性估计值和已发表算法的准确性。我们使用一组独立的图像验证了Deep-Masker的性能。Deep-Masker可以是一种准确、精确、全自动的方法，用于标准化肺部疾病小鼠模型中的弦长测量。
Chord length is an indirect measure of alveolar size and a critical endpoint in animal models of chronic obstructive pulmonary disease (COPD). In assessing chord length, the lumens of nonalveolar structures are eliminated from measurement by various methods, including manual masking. However, manual masking is resource intensive and can introduce variability and bias. We created a fully automated deep learning–based tool to mask murine lung images and assess chord length to facilitate mechanistic and therapeutic discovery in COPD called Deep-Masker (available at http://47.93.0.75:8110/login). We trained the deep learning algorithm for Deep-Masker using 1,217 images from 137 mice from 12 strains exposed to room air or cigarette smoke for 6 months. We validated this algorithm against manual masking. Deep-Masker demonstrated high accuracy with an average difference in chord length compared with manual masking of −0.3 ± 1.4% (rs = 0.99) for room-air–exposed mice and 0.7 ± 1.9% (rs = 0.99) for cigarette-smoke–exposed mice. The difference between Deep-Masker and manually masked images for change in chord length because of cigarette smoke exposure was 6.0 ± 9.2% (rs = 0.95). These values exceed published estimates for interobserver variability for manual masking (rs = 0.65) and the accuracy of published algorithms by a significant margin. We validated the performance of Deep-Masker using an independent set of images. Deep-Masker can be an accurate, precise, fully automated method to standardize chord length measurement in murine models of lung disease.