Whole-Body Soft-Tissue Lesion Tracking and Segmentation in Longitudinal CT Imaging Studies

Alessa Hering, Felix Peisen, Teresa Amaral, Sergios Gatidis, Thomas Eigentler, Ahmed Othman, Jan Hendrik Moltz
Proceedings of the Fourth Conference on Medical Imaging with Deep Learning, PMLR 143:312-326, 2021.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v143-hering21a, title = {Whole-Body Soft-Tissue Lesion Tracking and Segmentation in Longitudinal {CT} Imaging Studies}, author = {Hering, Alessa and Peisen, Felix and Amaral, Teresa and Gatidis, Sergios and Eigentler, Thomas and Othman, Ahmed and Moltz, Jan Hendrik}, booktitle = {Proceedings of the Fourth Conference on Medical Imaging with Deep Learning}, pages = {312--326}, year = {2021}, editor = {Heinrich, Mattias and Dou, Qi and de Bruijne, Marleen and Lellmann, Jan and Schläfer, Alexander and Ernst, Floris}, volume = {143}, series = {Proceedings of Machine Learning Research}, month = {07--09 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v143/hering21a/hering21a.pdf}, url = {https://proceedings.mlr.press/v143/hering21a.html}, 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.} }
Endnote
%0 Conference Paper %T Whole-Body Soft-Tissue Lesion Tracking and Segmentation in Longitudinal CT Imaging Studies %A Alessa Hering %A Felix Peisen %A Teresa Amaral %A Sergios Gatidis %A Thomas Eigentler %A Ahmed Othman %A Jan Hendrik Moltz %B Proceedings of the Fourth Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2021 %E Mattias Heinrich %E Qi Dou %E Marleen de Bruijne %E Jan Lellmann %E Alexander Schläfer %E Floris Ernst %F pmlr-v143-hering21a %I PMLR %P 312--326 %U https://proceedings.mlr.press/v143/hering21a.html %V 143 %X 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.
APA
Hering, A., Peisen, F., Amaral, T., Gatidis, S., Eigentler, T., Othman, A. & Moltz, J.H.. (2021). Whole-Body Soft-Tissue Lesion Tracking and Segmentation in Longitudinal CT Imaging Studies. Proceedings of the Fourth Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 143:312-326 Available from https://proceedings.mlr.press/v143/hering21a.html.

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