Keywords: Lesion Tracking, Lesion Segmentation, Soft-Tissue Lesion, Longitudinal CT Studies
TL;DR: In this paper, we present a pipeline that automates the segmentation and measurement of matching lesions, given a one-click point annotation in the baseline lesion.
Abstract: 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.
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Paper Type: validation/application paper
Primary Subject Area: Application: Radiology
Secondary Subject Area: Segmentation
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