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Conference 12033 > Paper 12033-86
Paper 12033-86

Fully automated longitudinal tracking and in-depth analysis of the entire tumor burden: unlocking the complexity

In person: 23 February 2022 • 5:30 PM - 7:00 PM PST

Abstract

We present a novel approach for handling complex information of lesion segmentation in CT follow-up studies. The backbone of our approach is the computation of a longitudinal tumor tree. We perform deep learning based segmentation of all lesions for each time point in CT follow-up studies. Subsequently, follow-up images are registered to establish correspondence between the studies and trace tumors among time points, yielding tree-like relations. The tumor tree encodes the complexity of the individual disease progression. In addition, we present novel descriptive statistics and tools for correlating tumor volumes and RECIST diameters to analyze significance of various markers.

Presenter

Fraunhofer-Institut für Digitale Medizin MEVIS (Germany)
Presenter/Author
Fraunhofer-Institut für Digitale Medizin MEVIS (Germany)
Author
Fraunhofer-Institut für Digitale Medizin MEVIS (Germany)
Author
Fraunhofer-Institut für Digitale Medizin MEVIS (Germany)
Author
Benjamin Geisler
Fraunhofer-Institut für Digitale Medizin MEVIS (Germany)
Author
Stefan Kraß
Fraunhofer-Institut für Digitale Medizin MEVIS (Germany)
Author
Fraunhofer-Institut für Digitale Medizin MEVIS (Germany)