In follow-up CT examinations of cancer patients, therapy success is evaluated by estimating the change in tumor size. This process is time-consuming and error-prone. We present a pipeline that automates the segmentation and measurement of matching lesions, given a point annotation in the baseline lesion. First, a region around the point annotation is extracted, in which a deep-learning-based segmentation of the lesion is performed. Afterward, a registration algorithm finds the corresponding image region in the follow-up scan and the convolutional neural network segments lesions inside this region. In the final step, the corresponding lesion is selected. We evaluate our pipeline on clinical follow-up data comprising 125 soft-tissue lesions from 43 patients with metastatic melanoma. Our pipeline succeeded for 96% of the baseline and 80% of the follow-up lesions, showing that we have laid the foundation for an efficient quantitative follow-up assessment in clinical routine.
Whole-Body Soft-Tissue Lesion Tracking and Segmentation in Longitudinal CT Imaging Studies
A. Hering, F. Peisen, T. Amaral, S. Gatidis, T. Eigentler, A. Othman and J. Moltz
Medical Imaging with Deep Learning 2021.