Hui Liu, Zhiyi Wang, Xue Li, Peng Ge, Yanfeng Tuo, Xufen Xie
School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, China.
School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China; Liaoning Key Lab for Aquatic Processing Quality and Safety, Dalian 116034, China.
J Microbiol Methods. 2025 Sep;236:107185. doi: 10.1016/j.mimet.2025.107185. Epub 2025 Jun 24.
Colony counting plays a crucial role in evaluating food quality and safety. The segmentation of colony adhesion images can significantly enhance the accuracy of food safety assessments. To achieve high-precision segmentation of colony adhesion images, this paper presents a novel multi-scale feature deep-enhanced fusion network MFDF-UNet, specifically designed for colony adhesion image segmentation. The core of the network lies in the design of a self-similar fusion fractal structure, which recursively integrates layers to enhance the network's ability to extract, integrate, and transfer multi-scale feature information. The DEC (depth-enhanced connectivity) units and PF (progressive fusion) modules in each stage progressively accumulate detailed features, thus improving the network's capacity to handle complex structures. Additionally, the design strengthens the information transfer between different layers, ensuring consistency of features across multiple layers. This reduces the imbalance in feature information transfer that can occur when certain regions of the image contain prominent edges, textures, or structural features, while other areas are relatively blurred or lack distinct features.The MFDF-UNet model achieved an average segmentation accuracy of 77.95 %, precision of 97.55 %, and a mean intersection-over-union (mIoU) of 57.94 % on the AGAR-based hybrid colony adhesion segmentation test dataset. Compared to other deep learning methods, such as PSPNet, DeepLabv3+, SegFormer, YOLOv8, U-Net, and ResNet, MFDF-UNet outperforms the highest-performing ResUNet by 7.53 % in segmentation accuracy, improves precision by 1.5 %, and surpasses ResUNet by 4.82 % in mIoU.Although our model requires slightly more parameters and training time, the improvements in segmentation accuracy and image quality sufficiently justify the additional cost, demonstrating its potential for practical applications in colony adhesion segmentation.
菌落计数在评估食品质量和安全方面起着至关重要的作用。菌落黏附图像的分割可以显著提高食品安全评估的准确性。为了实现菌落黏附图像的高精度分割，本文提出了一种新颖的多尺度特征深度增强融合网络MFDF-UNet，专门用于菌落黏附图像分割。该网络的核心在于自相似融合分形结构的设计，它通过递归地整合各层来增强网络提取、整合和传递多尺度特征信息的能力。每个阶段的DEC（深度增强连通性）单元和PF（渐进融合）模块逐步积累详细特征，从而提高网络处理复杂结构的能力。此外，该设计加强了不同层之间的信息传递，确保多层特征的一致性。这减少了图像某些区域包含突出边缘、纹理或结构特征而其他区域相对模糊或缺乏明显特征时可能出现的特征信息传递不平衡问题。MFDF-UNet模型在基于琼脂的混合菌落黏附分割测试数据集上实现了平均分割准确率77.95%、精确率97.55%以及平均交并比（mIoU）57.94%。与其他深度学习方法，如PSPNet、DeepLabv3+、SegFormer、YOLOv8、U-Net和ResNet相比，MFDF-UNet在分割准确率上比表现最佳的ResUNet高出7.53%，精确率提高了1.5%，在mIoU上比ResUNet高出4.82%。尽管我们的模型需要略多的参数和训练时间，但分割准确率和图像质量的提升足以证明额外成本的合理性，表明其在菌落黏附分割实际应用中的潜力。
