[论文解读] Long-Bone Fracture Detection using Artificial Neural Networks based on Contour Features of X-ray Images

Alice Yi Yang, Ling Cheng|arXiv (Cornell University)|Feb 21, 2019

AI in cancer detection参考文献 16被引用 12

一句话总结

本文提出两种基于轮廓的人工神经网络（ANN）模型——标准轮廓直方图特征法（CHFB）与改进型CHFB——用于X射线图像中的长骨骨折检测，利用19种提取的轮廓特征，包括梯度出现频率及0°梯度引导的区域分离。改进型CHFB方案排除了软组织轮廓，实现了82.98%的平均准确率与0.8275的AUC，优于标准版本，在灵敏度和假阴性控制方面表现更优。

ABSTRACT

The following paper proposes two contour-based fracture detection schemes. The development of the contour-based fracture is based on the line-based fracture detection schemes proposed in arXiv:1902.07458. Existing Computer Aided Diagnosis (CAD) systems commonly employs Convolutional Neural Networks (CNN), although the cost to obtain a high accuracy is the amount of training data required. The purpose of the proposed schemes is to obtain a high classification accuracy with a reduced number of training data through the use of detected contours in X-ray images. There are two contour-based fracture detection schemes. The first is the Standard Contour Histogram Feature-Based (CHFB) and the second is the improved CHFB scheme. The difference between the two schemes is the removal of the surrounding detected flesh contours from the leg region in the improved CHFB scheme. The flesh contours are automatically classified as non-fractures. The contours are further refined to give a precise representation of the image edge objects. A total of 19 features are extracted from each refined contour. 8 out of the 19 features are based on the number of occurrences for particular detected gradients in the contour. Moreover, the occurrence of the 0-degree gradient in the contours are employed for the separation of the knee, leg and foot region. The features are a summary representation of the contour, in which it is used as inputs into the Artificial Neural Network (ANN). Both Standard CHFB and improved CHFB schemes are evaluated with the same experimental set-ups. The average system accuracy for the Standard CHFB scheme is 80.7%, whilst the improved CHFB scheme has an average accuracy of 82.98%. Additionally, the hierarchical clustering technique is adopted to highlight the fractured region within the X-ray image, using extracted 0-degree gradients from fractured contours.

研究动机与目标

• 通过利用基于轮廓的特征而非原始像素数据，减少医学X射线骨折检测对大规模训练数据集的依赖。
• 在数据需求最小化的前提下，利用人工神经网络提升骨折检测的分类准确率与灵敏度。
• 通过优化边缘表示并去除无关的软组织轮廓，实现对骨折与非骨折轮廓的区分。
• 通过检测轮廓中0°梯度特征的分层聚类，提升骨折区域的视觉定位能力。
• 通过优化特征选择与聚类策略，最小化假阴性率——在临床环境中至关重要。

提出的方法

• 使用边缘检测从X射线图像中提取轮廓，并通过去除冗余或重复点进行轮廓优化。
• 从每个轮廓中提取19项特征，包括特定梯度方向（尤其是0°梯度）的频率，以识别膝部、腿部和足部等解剖区域。
• 应用主成分分析（PCA）识别最能区分骨折与非骨折轮廓的主导特征。
• 在轮廓特征上训练人工神经网络（ANN），将每个轮廓分类为骨折或非骨折。
• 对检测到的骨折轮廓中的0°梯度点进行分层聚类，以突出并定位图像中的骨折区域。
• 实施双重评估：一项基于具有图像上下文的轮廓，另一项基于随机选择的非上下文轮廓，以测试模型的泛化能力。

实验结果

研究问题

• RQ1 基于轮廓的特征提取是否能够降低X射线成像中训练准确骨折检测模型的数据需求？
• RQ2 在ANN基骨折检测系统中，去除非骨骼（软组织）轮廓在多大程度上提升了性能与灵敏度？
• RQ3 轮廓中0°梯度特征在多大程度上通过分层聚类有助于定位骨折区域？
• RQ4 在轮廓分类过程中引入图像上下文信息在多大程度上影响了系统的整体准确率与鲁棒性？
• RQ5 在不牺牲特异性或整体准确率的前提下，是否能够将基于轮廓的ANN系统中的假阴性率最小化？

主要发现

• 改进型CHFB方案在两种评估中均实现了82.98%的平均准确率，优于标准CHFB方案的80.7%。
• 改进型CHFB方案表现出更高的灵敏度，假阴性与假阳性比率为1:0.94，优于标准方案的1:0.71。
• 改进型CHFB方案的AUC为0.8275，略高于标准方案的0.8225，表明其在真正例检测性能方面更优。
• 改进型CHFB方案实现的最大准确率为89.07%，表明通过更精细的数据标注仍有进一步提升的潜力。
• 对0°梯度点进行分层聚类能有效突出骨折区域，但仅靠轮廓定位仍不足，因轮廓常过度延伸至非骨折区域。
• 改进型方案中假阳性减少，但假阴性仍是挑战，表明在特征选择与聚类层级自适应方面仍有优化空间。