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Abstract #2462

A Simple Fully Automated Method for Skull-Stripping Quality Control in Brain MR Image Processing Pipelines Evaluated Using Multicenter Data

Till Huelnhagen1,2,3, Ricardo Corredor-Jerez1,2,3, Claudia Bigoni1, Veronica Ravano1, Mário João Fartaria1,2,3, Adrian Tsang4, Rodrigo D. Perea4, Sara Makaretz4, Maria Laura Blefari4, Yuchuan Zhuang4, Bénédicte Maréchal1,2,3, Elizabeth Fisher4, and Tobias Kober1,2,3

1Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, 2Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland, 3Signal Processing Laboratory (LTS 5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 4Biogen, Cambridge, MA, United States

Automated brain segmentation approaches are increasingly being used for decision support in routine clinical settings. While segmentation may be considered a “solved problem” in research, it is still challenging to assure reliable performance of automated tools in clinical settings, which is a crucial requirement for diagnostic tools. To ensure correct results, automated quality control procedures are of vital importance, but they are often difficult to implement or time-consuming to run. We propose a simple and fast fully automated method to detect segmentation errors, and we evaluate its performance to detect skull-stripping-errors using results of two different brain segmentation algorithms on a large multicenter dataset. Results show that the method is able to detect skull-stripping-errors with high specificity.

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