A multi-output deep learning algorithm to improve brain lesion segmentation by enhancing the resistance of variabilities in tissue contrast
Yi-Tien Li, Hsiao‐Wen Chung, David J. Chen · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · 2023
We propose a multi-output segmentation approach, which incorporates other non-lesion brain tissue maps into the additional output layers to force the model to learn more about the lesion and tissue characteristics. We construct a cross-vendor study by training the white matter hyperintensities segmentation model on cases collected from one vendor and testing the model performance on eight different data sets. The model performance can be significantly improved, especially in testing sets which shows low image contrast similarity with training data, suggesting the feasibility of incorporating the non-lesion characteristics into segmentation model to enhance the resistance of cross-vendor image contrast variabilities.