Monitoring treatment response in longitudinal studies plays an important role
in clinical practice. Accurately identifying lesions across serial imaging
follow-up is the core to the monitoring procedure. Typically this incorporates
both image and anatomical considerations. However, matching lesions manually is
labor-intensive and time-consuming. In this work, we present deep lesion
tracker (DLT), a deep learning approach that uses both appearance- and
anatomical-based signals. To incorporate anatomical constraints, we propose an
anatomical signal encoder, which prevents lesions being matched with visually
similar but spurious regions. In addition, we present a new formulation for
Siamese networks that avoids the heavy computational loads of 3D
cross-correlation. To present our network with greater varieties of images, we
also propose a self-supervised learning (SSL) strategy to train trackers with
unpaired images, overcoming barriers to data collection. To train and evaluate
our tracker, we introduce and release the first lesion tracking benchmark,
consisting of 3891 lesion pairs from the public DeepLesion database. The
proposed method, DLT, locates lesion centers with a mean error distance of 7
mm. This is 5% better than a leading registration algorithm while running 14
times faster on whole CT volumes. We demonstrate even greater improvements over
detector or similarity-learning alternatives. DLT also generalizes well on an
external clinical test set of 100 longitudinal studies, achieving 88% accuracy.
Finally, we plug DLT into an automatic tumor monitoring workflow where it leads
to an accuracy of 85% in assessing lesion treatment responses, which is only
0.46% lower than the accuracy of manual inputs.

本研究提出了基于深度学习的深度损伤跟踪器（DLT），该方法解决了长期研究中监测治疗反应的核心问题，即如何准确地对大量数据进行图像识别和解剖学研究，同时提高检测的速度和精度，该方法已在公共 DeepLesion 数据库上进行了测试和验证，并成功应用于肿瘤监测工作流程中。