Radiomic Feature Stability Analysis Based on Probabilistic Segmentations

IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1188–1192

Abstract

Identifying image features that are robust with respect to segmentation variability and domain shift is a tough challenge in radiomics. So far, this problem has mainly been tackled in test-retest analyses. In this work we analyze radiomics feature stability based on probabilistic segmentations. Based on a public lung cancer dataset, we generate an arbitrary number of plausible segmentations using a Probabilistic U-Net. From these segmentations, we extract a high number of plausible feature vectors for each lung tumor and analyze feature variance with respect to the segmentations. Our results suggest that there are groups of radiomic features that are more (e.g. statistics features) and less (e.g. gray-level size zone matrix features) robust against segmentation variability. Finally, we demonstrate that segmentation variance impacts the performance of a prognostic lung cancer survival model and propose a new and potentially more robust radiomics feature selection workflow.

Authors 7

1. Christoph Haarburger Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision, RWTH Aachen University, Germany

   Institute of Imaging and Computer Vision, RWTH Aachen University,Germany

2. Justus Schock

   Düsseldorf University Hospital

   Affiliation as printed

   University Hospital Düsseldorf, Germany

   University Hospital Düsseldorf,Department of Diagnostic and Interventional Radiology,Germany

3. Daniel Truhn Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision, RWTH Aachen University, Germany

   Institute of Imaging and Computer Vision, RWTH Aachen University,Germany

4. Philippe Weitz

   Karolinska Institutet

   Affiliation as printed

   Karolinska Institutet, Stockholm, Sweden

   Karolinska Institutet Department of Medical Epidemiology and Biostatistics Stockholm Sweden

5. Gustav Mueller-Franzes Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision, RWTH Aachen University, Germany

   Institute of Imaging and Computer Vision, RWTH Aachen University,Germany

6. Leon Weninger Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision, RWTH Aachen University, Germany

   Institute of Imaging and Computer Vision, RWTH Aachen University,Germany

7. Dorit Merhof Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision, RWTH Aachen University, Germany

   Institute of Imaging and Computer Vision, RWTH Aachen University,Germany