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FU-Net: fast biomedical image segmentation model based on bottleneck convolution layers

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Abstract

Recently, the introduction of Convolutional Neural Network (CNNs) has advanced the way of solving image segmentation tasks. Semantic image segmentation has considerably benefited from employing various CNN models. The most widely used network in this field is U-Net and its different variations. However, these models require significant number of trainable parameters, floating-point operations per second, and great computational power to be trained. These factors make real-time semantic segmentation in low powered devices very hard. Therefore, in the present paper, we aim to modify particular aspects of the U-Net model to improve its performance through developing a fast U-Net (FU-Net) relying on bottleneck convolution layers in the contraction and expansion paths of the model. The proposed model can be utilized in semantic segmentation applications even on the devices with limited computational power and memory by ensuring the state-of-the-art performance. The amount of memory required by the proposed model is reduced by 23 times when compared with the original U-Net. Moreover, the modifications allowed achieving better performance. In conducted experiments, we assessed the performance of the proposed model on two biomedical image segmentation datasets, namely 2018 Data Science Bowl and ICIS 2018: Skin Lesion Analysis Towards Melanoma Detection. FU-Net demonstrated the state-of-the-art results in biomedical image segmentation, requiring the number of trainable parameters reduced by eight times compared with the original U-Net model. In addition, using bottleneck layers decreased the number of computations, resulting in nearly 30% speed-up at the training, validation and test stages. Furthermore, despite relying on fewer parameters FU-Net achieved a slight improvement of the performance in terms of pixel accuracy, Jaccard index, and dice coefficient evaluation metrics.

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Acknowledgements

This study was supported by the BK21 Plus project (SW Human Resource Development Program for Supporting Smart Life) funded by the Ministry of Education, School of Computer Science and Engineering, Kyungpook National University, Korea (21A20131600005).

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This research received no external funding.

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Authors and Affiliations

  1. The School of Computer Science and Engineering, Kyungpook National University, Daegu, 41566, South Korea

    Bekhzod Olimov, Karshiev Sanjar, Sadia Din, Awaise Ahmad, Anand Paul & Jeonghong Kim

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  1. Bekhzod Olimov
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  2. Karshiev Sanjar
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  3. Sadia Din
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  4. Awaise Ahmad
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  5. Anand Paul
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  6. Jeonghong Kim
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Contributions

Conceptualization, OB; data curation, KS; Formal analysis, SD; Funding acquisition, JK; Investigation, AA; Methodology, OB; Project administration, JK; Resources, SK; Software, OB; Validation, AP; Writing—original draft, OB; Writing—review, JK.

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Correspondence to Jeonghong Kim.

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Olimov, B., Sanjar, K., Din, S. et al. FU-Net: fast biomedical image segmentation model based on bottleneck convolution layers. Multimedia Systems 27, 637–650 (2021). https://doi.org/10.1007/s00530-020-00726-w

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