Abstract：
Considered a neglected tropical pathology, Chagas disease is responsible for thousands of deaths per year and it is caused by the parasite Trypanosoma cruzi. Since many infected people can remain asymptomatic, a fast diagnosis is necessary for proper intervention. Parasite microscopic observation in blood samples is the gold standard method to diagnose Chagas disease in its initial phase; however, this is a time-consuming procedure, requires expert intervention, and there is currently no efficient method to automatically perform this task. Therefore, we propose an efficient residual convolutional neural network, named Res2Unet, to perform a semantic segmentation of Trypanosoma cruzi parasites, with an active contour loss and improved residual connections, whose design is based on Heun\'s method for solving ordinary differential equations. The model was trained on a dataset of 626 blood sample images and tested on a dataset of 207 images. Validation experiments report that our model achieved a Dice coefficient score of 0.84, a precision value of 0.85, and a recall value of 0.82, outperforming current state-of-the-art methods. Since Chagas disease is a severe and silent illness, our computational model may benefit health care providers to give a prompt diagnose for this worldwide affection.
摘要：
被认为是被忽视的热带病理学，查加斯病每年造成数千人死亡，它是由寄生虫克氏锥虫引起的。由于许多感染者可以保持无症状，快速诊断对于适当的干预是必要的。血液样本中的寄生虫显微镜观察是诊断查加斯病初期的金标准方法；然而，这是一个耗时的过程，需要专家干预,，并且目前没有有效的方法来自动执行此任务。因此,我们提出了一种有效的残差卷积神经网络，名为Res2Unet，为了对克氏锥虫寄生虫进行语义分割，具有主动轮廓损失和改进的剩余连接，其设计基于Heun的常微分方程求解方法。该模型在626个血液样本图像的数据集上进行训练,并在207个图像的数据集上进行测试。验证实验报告说，我们的模型实现了Dice系数得分为0.84，精度值为0.85，召回值为0.82，优于当前最先进的方法。由于恰加斯病是一种严重而无声的疾病，我们的计算模型可能有利于卫生保健提供者对这一世界性影响做出及时诊断.
