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.

中文翻译：

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FU-Net：基于瓶颈卷积层的快速生物医学图像分割模型

最近，卷积神经网络 (CNN) 的引入推动了解决图像分割任务的方法。语义图像分割大大受益于采用各种 CNN 模型。该领域使用最广泛的网络是 U-Net 及其不同的变体。然而，这些模型需要大量的可训练参数、每秒浮点运算和强大的计算能力来训练。这些因素使得低功耗设备中的实时语义分割变得非常困难。因此，在本文中，我们的目标是通过在模型的收缩和扩展路径中依赖瓶颈卷积层开发快速 U-Net (FU-Net) 来修改 U-Net 模型的特定方面以提高其性能。通过确保最先进的性能，即使在计算能力和内存有限的设备上，所提出的模型也可以用于语义分割应用中。与原始 U-Net 相比，所提出模型所需的内存量减少了 23 倍。此外，这些修改允许实现更好的性能。在进行的实验中，我们评估了所提出模型在两个生物医学图像分割数据集上的性能，即 2018 Data Science Bowl 和 ICIS 2018：Skin Lesion Analysis Towards Melanoma Detection。FU-Net 在生物医学图像分割方面展示了最先进的结果，与原始 U-Net 模型相比，可训练参数的数量减少了 8 倍。此外，使用瓶颈层减少了计算次数，导致在训练、验证和测试阶段的速度提高了近 30%。此外，尽管依赖较少的参数，FU-Net 在像素精度、Jaccard 指数和骰子系数评估指标方面的性能略有提高。