论文标题
使用坐标卷积对超声的深度学习分割
Deep Learning-based Segmentation of Pleural Effusion From Ultrasound Using Coordinate Convolutions
论文作者
论文摘要
在许多低到中型收入（LMIC）国家中，超声用于评估胸腔积液。通常，积液的程度是由超声检查员手动测量的，导致明显的内部/观察者间变异性。在这项工作中，我们研究了深度学习（DL）以自动化超声图像中胸腔积液分割的过程。在在LMIC设置中获得的两个数据集上，我们使用NNU-NET DL模型获得了中位数骰子相似性系数（DSC）和0.82和0.74。我们还研究了DL模型中坐标卷积的使用，发现这会导致第一个数据集中的中位数DSC在0.85上的统计学显着改善，而第二个数据集则没有显着更改。这项工作首次展示了DL在LMIC环境中从超声检查中自动化的积液评估过程的潜力，在LMIC环境中，通常缺乏经验丰富的放射科医生来执行此类任务。
In many low-to-middle income (LMIC) countries, ultrasound is used for assessment of pleural effusion. Typically, the extent of the effusion is manually measured by a sonographer, leading to significant intra-/inter-observer variability. In this work, we investigate the use of deep learning (DL) to automate the process of pleural effusion segmentation from ultrasound images. On two datasets acquired in a LMIC setting, we achieve median Dice Similarity Coefficients (DSCs) of 0.82 and 0.74 respectively using the nnU-net DL model. We also investigate the use of coordinate convolutions in the DL model and find that this results in a statistically significant improvement in the median DSC on the first dataset to 0.85, with no significant change on the second dataset. This work showcases, for the first time, the potential of DL in automating the process of effusion assessment from ultrasound in LMIC settings where there is often a lack of experienced radiologists to perform such tasks.
