背景:在医学成像中,基于深度学习的语义分割算法与预处理技术的集成可以减少对人类注释的需求并推进疾病分类。在已建立的预处理技术中,对比度有限的自适应直方图均衡(CLAHE)已经证明了在各种模态中改进分割算法的功效。如X射线和CT。然而,考虑到数据集的异质性和不同解剖结构的各种对比,仍然需要改进的对比增强方法。
方法:本研究提出了一种新的预处理技术,ps-KDE,研究其对深度学习算法在前后胸部X线片中分割主要器官的影响。Ps-KDE通过基于所有图像的归一化频率替换像素值来增强图像对比度。我们在U-Net架构上评估了我们的方法,并在ImageNet上预先训练了ResNet34骨干。训练五个独立的模型来分割心脏,左肺,右肺,左锁骨,和右锁骨.
结果:使用ps-KDE训练来分割左肺的模型获得了0.780(SD=0.13)的Dice评分,而接受CLAHE训练的Dice得分为0.717(SD=0.19),p<0.01。ps-KDE似乎也更健壮,因为基于CLAHE的模型在左肺模型的选择测试图像中对右肺进行了错误分类。执行ps-KDE的算法可在https://github.com/wyc79/ps-KDE获得。
结论:我们的结果表明,当分割某些肺区域时,ps-KDE比当前的预处理技术具有优势。这在随后的分析如疾病分类和风险分层中可能是有益的。
BACKGROUND: In medical imaging, the integration of deep-learning-based semantic segmentation algorithms with preprocessing techniques can reduce the need for human annotation and advance disease classification. Among established preprocessing techniques, Contrast Limited Adaptive Histogram Equalization (CLAHE) has demonstrated efficacy in improving segmentation algorithms across various modalities, such as X-rays and CT. However, there remains a demand for improved contrast enhancement methods considering the heterogeneity of datasets and the various contrasts across different anatomic structures.
METHODS: This study proposes a novel preprocessing technique, ps-KDE, to investigate its impact on deep learning algorithms to segment major organs in posterior-anterior chest X-rays. Ps-KDE augments image contrast by substituting pixel values based on their normalized frequency across all images. We evaluate our approach on a U-Net architecture with ResNet34 backbone pre-trained on ImageNet. Five separate models are trained to segment the heart, left lung, right lung, left clavicle, and right clavicle.
RESULTS: The model trained to segment the left lung using ps-KDE achieved a Dice score of 0.780 (SD = 0.13), while that of trained on CLAHE achieved a Dice score of 0.717 (SD = 0.19), p<0.01. ps-KDE also appears to be more robust as CLAHE-based models misclassified right lungs in select test images for the left lung model. The algorithm for performing ps-KDE is available at https://github.com/wyc79/ps-KDE.
CONCLUSIONS: Our results suggest that ps-KDE offers advantages over current preprocessing techniques when segmenting certain lung regions. This could be beneficial in subsequent analyses such as disease classification and risk stratification.