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Abstract:
Automatic image segmentation at magnetic resonance imaging (MRI) of the brain is essential for a number of applications. Many well-known segmentation tools exist for the clinical domain. However, we have found that they become unreliable when applied to ultra-high resolution images and, in particular, to data acquired at magnetic field strength of 9.4 T. This has motivated us to develop a segmentation method that can handle images at ultra-high resolution ≤ 0.6 mm and field strengths 1.5–9.4 T. Specifically, we propose an adversarial game for flexible domain adaptation of convolutional neural networks in the context of brain MRI segmentation. In particular, we develop FLEXseg, the first brain MRI segmentation method suitable for images acquired at 9.4 T with 0.6 mm isotropic resolution. We demonstrate the performance of FLEXseg by comparing it with manually corrected labels approved by expert neuroradiologists.
