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Abstract #3566

Retrofitting a Brain Segmentation Algorithm with Deep Learning Techniques: Validation and Experiments

Punith B Venkategowda1, Asha K Kumaraswamy1,2, Jonas Richiardi3,4,5, Sanjeev Krishnan Thampi1, Tobias Kober3,4,5, Bénédicte Maréchal3,4,5, and Ricardo A. Corredor-Jerez3,4,5
1Siemens Healthcare Pvt. Ltd., Bangalore, India, 2Vidyavardhaka College of Engineering, Mysuru, India, 3Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, 4Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland, 5Signal Processing Laboratory (LTS 5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

Deep learning techniques have proved their robustness in solving medical image analysis problems. This study proposes a conservative approach to benefit from the use of these methods to incrementally improve the performance of a well-established brain segmentation method. For this purpose, convolutional neural networks are trained to perform a reliable skull-stripping, based on weak labels of the original algorithm. The performance of the new pipeline is evaluated in a large cohort of dementia patients and healthy controls. The results present significant improvements in reproducibility and computation speed, while preserving accuracy and power of discrimination between groups.

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