Ensembling mitigates scanner effects in deep learning medical image segmentation with deep-U-Nets

Abstract

Machine learning algorithms tend to perform better within the setting wherein they are trained, a phenomenon known as the domain effect. Deep learning-based medical image segmentation algorithms are often trained using data acquired from specific scanners; however, these algorithms are expected to accurately segment anatomy in images acquired from scanners different from the ones used to obtain training images for such algorithms. In this work, we present evidence of a scanner and magnet strength specific domain effect for a deep-U-Net trained to segment spinal canals on axial MR images. The trained network performs better on new data from the same scanner and worse on data from other scanners, demonstrating a scanner-specific domain effect. We then construct ensembles of the U-Nets, in which each U-Net in the ensemble differs from others only in initialization. Finally, we demonstrate that these UNet ensembles reduce the differential between in-domain and out-of-domain performance, thereby mitigating the domain effect associated with single U-Nets. Our study evidences the importance of developing software robust to scanner-specific domain effects to handle scanner bias in Deep Learning.

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