TY - JOUR
T1 - Whole-Body Soft-Tissue Lesion Tracking and Segmentation in Longitudinal CT Imaging Studies
AU - Hering, Alessa Denise
AU - Peisen, Felix
AU - Amaral, Teresa
AU - Gatidis, Sergios
AU - Eigentler, Thomas
AU - Othman, Ahmed
AU - Moltz, Jan Hendrik
N1 - PMLR 143:312-326, 2021
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
M3 - Journal articles
VL - 2021
JO - Proceedings of Machine Learning Research
JF - Proceedings of Machine Learning Research
IS - Volume 143
ER -