基于多级深度卷积编码解码网络的贫血红细胞语义分割
Semantic Segmentation of Anaemic RBCs Using Multilevel Deep Convolutional Encoder-Decoder Network
摘要：
血液图像的像素级分析在血液相关疾病，尤其是贫血的诊断中起着至关重要的作用。这些分析主要依赖于形态畸形的准确诊断，如形状，大小和精确的像素计数。在传统的分割方法中，采用了基于实例或对象的方法，这对于像素级分析是不可行的。卷积神经网络（CNN）模型需要具有详细像素级信息的大型数据集，用于深度学习领域中的红细胞语义分割。在目前的研究工作中，我们通过提出一个多级深度卷积编码器-解码器网络以及两个最先进的健康和贫血RBC数据集来解决这些问题。提出的多层CNN模型保留了一层中提取的像素级语义信息，然后传递到下一层以选择相关特征。这种现象有助于对健康和贫血红细胞成分进行精确的像素级计数以及形态分析。出于实验目的，我们提出了两个最先进的RBC数据集，即，健康RBC和贫血RBC数据集。每个数据集包含1000张图像、真实掩模、相关、全血细胞计数（CBC）和形态学报告，用于性能评估。使用交叉匹配分析和地面真实掩模通过使用05倍训练程序找到IoU、个体训练、验证、测试准确度和全局准确度来评估所提出的模型结果。该模型在Healthy-RBC数据集上的训练、验证和测试精度分别为0.9856、0.9760和0.9720，在Anaemic-RBC数据集上的训练、验证和测试精度分别为0.9736、0.9696和0.9591。该模型在健康和贫血数据集上的IoU和BFScore分别为0.9311，0.9138和0.9032，0.8978。
英文摘要：
Pixel-level analysis of blood images plays a pivotal role in diagnosing blood-related diseases, especially Anaemia. These analyses mainly rely on an accurate diagnosis of morphological deformities like shape, size, and precise pixel counting. In traditional segmentation approaches, instance or object-based approaches have been adopted that are not feasible for pixel-level analysis. The convolutional neural network (CNN) model required a large dataset with detailed pixel-level information for the semantic segmentation of red blood cells in the deep learning domain. In current research work, we address these problems by proposing a multi-level deep convolutional encoder-decoder network along with two state-of-the-art healthy and Anaemic-RBC datasets. The proposed multi-level CNN model preserved pixel-level semantic information extracted in one layer and then passed to the next layer to choose relevant features. This phenomenon helps to precise pixel-level counting of healthy and anaemic-RBC elements along with morphological analysis. For experimental purposes, we proposed two state-of-the-art RBC datasets, i.e., Healthy-RBCs and Anaemic-RBCs dataset. Each dataset contains 1000 images, ground truth masks, relevant, complete blood count (CBC), and morphology reports for performance evaluation. The proposed model results were evaluated using crossmatch analysis with ground truth mask by finding IoU, individual training, validation, testing accuracies, and global accuracies using a 05-fold training procedure. This model got training, validation, and testing accuracies as 0.9856, 0.9760, and 0.9720 on the Healthy-RBC dataset and 0.9736, 0.9696, and 0.9591 on an Anaemic-RBC dataset. The IoU and BFScore of the proposed model were 0.9311, 0.9138, and 0.9032, 0.8978 on healthy and anaemic datasets, respectively.
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