Dual-Attention Enhanced BDense-UNet for Liver Lesion Segmentation

• URL: http://arxiv.org/abs/2107.11645v1
• Date: Sat, 24 Jul 2021 16:28:00 GMT
• Title: Dual-Attention Enhanced BDense-UNet for Liver Lesion Segmentation
• Authors: Wenming Cao, Philip L.H. Yu, Gilbert C.S. Lui, Keith W.H. Chiu, Ho-Ming Cheng, Yanwen Fang, Man-Fung Yuen, Wai-Kay Seto
• Abstract summary: We propose a new segmentation network by integrating DenseUNet and bidirectional LSTM together with attention mechanism, termed as DA-BDense-UNet. DenseUNet allows learning enough diverse features and enhancing the representative power of networks by regulating the information flow.
• Score: 3.1667381240856987
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we propose a new segmentation network by integrating DenseUNet and bidirectional LSTM together with attention mechanism, termed as DA-BDense-UNet. DenseUNet allows learning enough diverse features and enhancing the representative power of networks by regulating the information flow. Bidirectional LSTM is responsible to explore the relationships between the encoded features and the up-sampled features in the encoding and decoding paths. Meanwhile, we introduce attention gates (AG) into DenseUNet to diminish responses of unrelated background regions and magnify responses of salient regions progressively. Besides, the attention in bidirectional LSTM takes into account the contribution differences of the encoded features and the up-sampled features in segmentation improvement, which can in turn adjust proper weights for these two kinds of features. We conduct experiments on liver CT image data sets collected from multiple hospitals by comparing them with state-of-the-art segmentation models. Experimental results indicate that our proposed method DA-BDense-UNet has achieved comparative performance in terms of dice coefficient, which demonstrates its effectiveness.

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