A multiple gated boosting network for multi‐organ medical image segmentation

Abstract

Abstract Segmentations provide important clues for diagnosing diseases. U‐shaped neural networks with skip connections have become one of popular frameworks for medical image segmentation. Skip connections really reduce loss of spatial details caused by down‐sampling, but they cannot handle well semantic gaps between low‐ and high‐level features. It is quite challenging to accurately separate out long, narrow, and small organs from human bodies. To solve these problems, the authors propose a Multiple Gated Boosting Network (MGB‐Net). To boost spatial accuracy, the authors first adopt Gated Recurrent Units (GRU) to design multiple Gated Skip Connections (GSC) at different levels, which efficiently reduce the semantic gap between the shallow and deep features. The Update and Reset gates of GRUs enhance features beneficial to segmentation and suppress information adverse to final results in a recurrent way. To obtain more scale invariances, the authors propose a module of Multi‐scale Weighted Channel Attention (MWCA). The module first uses convolutions with different kernel sizes and group numbers to generate multi‐scale features, and then adopts learnable weights to emphasize the importance of each scale for capturing attention features. Blocks of Transformer Self‐Attention (TSA) are sequentially stacked to extract long‐range dependency features. To effectively fuse and boost the features of MWCA and TSA, the authors use GRUs again to propose a Gated Dual Attention module (GDA), which enhances beneficial features and suppresses adverse information in a gated learning way. Experiments show that the authors’ method achieves an average Dice coefficient of 80.66% on the Synapse multi‐organ segmentation dataset. The authors’ method outperforms the state‐of‐the‐art methods on medical images. In addition, the authors’ method achieves a Dice segmentation accuracy of 62.77% on difficult objects such as pancreas, significantly exceeding the current average accuracy, so multiple gated boosting (MGB) methods are reliably effective for improving the ability of feature representations. The authors’ code is publicly available at https://github.com/DAgalaxy/MGB‐Net.

Keywords

• medical image processing
• transforms