MFDF-UNet: Multiscale feature depth-enhanced fusion network for colony adhesion image segmentation

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.