Faculty of Electrical Engineering, Shahrood University of Technology, Daneshgah Blvd., P.O. Box: 3619995161, Shahrood, Iran.
Esfarayen Faculty of Medical Sciences, Esfarayen, Iran.
Tissue Cell. 2022 Jun;76:101816. doi: 10.1016/j.tice.2022.101816. Epub 2022 May 7.
Anthrax is a severe infectious disease caused by the Bacillus anthracis bacterium. This paper aims to design and implement a fast and reliable system based on microscopic image processing of patient tissue samples for the automatic diagnosis of anthrax and other tissues diseases, metastasis detection, patient prognosis, etc. An improved UNet++ architecture is proposed to segment microscopic images of patient tissue samples. The proposed model combines multi-scale features by adding skip connections in two paths; the forward path from the encoder to the decoder and the decoder path to the output. These new connections improve the performance of the UNet++. Integration of the squeeze and excitation-inception blocks in the new skip connections provides the network with features at different scales with different kernel sizes. Several convolutional networks are used as the backbone to extract powerful representations in the encoder section. The use of batch normalization, dropout technique, and LRelu activation function in this model accelerates convergence and increases the generalization power of the model. To overcome the problem of data imbalance of different classes, a weighted hybrid loss function is proposed, which further improved segmentation efficiency. The semantic segmentation results are converted to the instance segmentation using the marker-based watershed algorithm. Experimental results show that despite many challenges of microscopic image analysis, the proposed model is a reliable system for the automatic diagnosis of anthrax and other tissues diseases. It produces better results than state-of-the-art architectures.
炭疽是一种由炭疽杆菌引起的严重传染病。本文旨在设计和实现一种基于患者组织样本显微镜图像处理的快速可靠系统，用于炭疽和其他组织疾病的自动诊断、转移检测、患者预后等。提出了一种改进的 UNet++ 架构来对患者组织样本的显微镜图像进行分割。所提出的模型通过在两条路径（从编码器到解码器的前向路径和解码器到输出的路径）中添加跳过连接来组合多尺度特征。这些新的连接提高了 UNet++的性能。在新的跳过连接中集成 squeeze 和 excitation-inception 块，为网络提供了具有不同内核大小的不同尺度的特征。几个卷积网络被用作编码器部分的骨干网络，以提取强大的表示。在该模型中使用批量归一化、dropout 技术和 LRelu 激活函数，加速了收敛并提高了模型的泛化能力。为了克服不同类别的数据不平衡问题，提出了一种加权混合损失函数，进一步提高了分割效率。使用基于标记的分水岭算法将语义分割结果转换为实例分割。实验结果表明，尽管存在许多显微镜图像分析的挑战，但所提出的模型是一种用于炭疽和其他组织疾病自动诊断的可靠系统。它的结果优于最先进的架构。
