Owens William, Wiegand Raymond, Studin Mark, Capoferri Donald, Barooha Kenneth, Greaux Alexander, Rattray Robert, Hutton Adam, Cintineo John, Chaudhary Vipin
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:564-567. doi: 10.1109/EMBC.2017.8036887.
Automatic identification of specific osseous landmarks on the spinal radiograph can be used to automate calculations for correcting ligament instability and injury, which affect 75% of patients injured in motor vehicle accidents. In this work, we propose to use deep learning based object detection method as the first step towards identifying landmark points in lateral lumbar X-ray images. The significant breakthrough of deep learning technology has made it a prevailing choice for perception based applications, however, the lack of large annotated training dataset has brought challenges to utilizing the technology in medical image processing field. In this work, we propose to fine tune a deep network, Faster-RCNN, a state-of-the-art deep detection network in natural image domain, using small annotated clinical datasets. In the experiment we show that, by using only 81 lateral lumbar X-Ray training images, one can achieve much better performance compared to traditional sliding window detection method on hand crafted features. Furthermore, we fine-tuned the network using 974 training images and tested on 108 images, which achieved average precision of 0.905 with average computation time of 3 second per image, which greatly outperformed traditional methods in terms of accuracy and efficiency.
在脊柱X光片上自动识别特定的骨性标志可用于自动计算以校正韧带不稳定和损伤，此类情况影响75%的机动车事故受伤患者。在这项工作中，我们提议使用基于深度学习的目标检测方法作为在腰椎侧位X光图像中识别标志点的第一步。深度学习技术的重大突破使其成为基于感知的应用的普遍选择，然而，缺乏大量带注释的训练数据集给在医学图像处理领域利用该技术带来了挑战。在这项工作中，我们提议使用小的带注释的临床数据集对深度网络Faster-RCNN（自然图像领域中最先进的深度检测网络）进行微调。在实验中我们表明，仅使用81张腰椎侧位X光训练图像，与基于手工特征的传统滑动窗口检测方法相比就能实现更好的性能。此外，我们使用974张训练图像对网络进行微调并在108张图像上进行测试，平均精度达到0.905，平均每张图像的计算时间为3秒，在准确性和效率方面大大优于传统方法。
