ATU-NET: An Adaptive Transformation-Based U-NET for Medical Image Segmentation

Abstract

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Both the U-Net and its variants, which produce the state-of-the-art performance in the field of medical image segmentation, are founded on an encoder-decoder architecture. However, this architecture generally processes the input image in the spatial domain only, overlooking potential insights that could be gained from other transform domains. For that, an adaptive transformation-based U-Net (ATU-Net) is proposed in this paper. Our ATU-Net is based on a novel network architecture called the adaptive transformation-encoder-decoder (ATU), which adaptively transforms the input image into a more suitable domain by training the transformation kernel for processing to make it easier for the encoder and decoder to extract key features of the image. Extensive experimental results obtained on benchmark datasets have shown that our proposed ATU-Net can deliver superior performance to the existing methods.