Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:2732-2735. doi: 10.1109/EMBC46164.2021.9629563.
Chagas disease is a widely spreaded illness caused by the parasite Trypanosoma cruzi (T. cruzi). Most cases go unnoticed until the accumulated myocardial damage affect the patient. The endomyocardium biopsy is a tool to evaluate sustained myocardial damage, but analyzing histopathological images takes a lot of time and its prone to human error, given its subjective nature. The following work presents a deep learning method to detect T. cruzi amastigotes on histopathological images taken from a endomyocardium biopsy during an experimental murine model. A U-Net convolutional neural network architecture was implemented and trained from the ground up. An accuracy of 99.19% and Jaccard index of 49.43% were achieved. The obtained results suggest that the proposed approach can be useful for amastigotes detection in histopathological images.Clinical relevance- The proposed method can be incorporated as automatic detection tool of amastigotes nests, it can be useful for the Chagas disease analysis and diagnosis.
恰加斯病是一种由寄生虫克氏锥虫（T. cruzi）引起的广泛传播的疾病。大多数病例在累积性心肌损伤影响患者之前未被察觉。心肌内膜活检是评估持续性心肌损伤的一种工具，但分析组织病理学图像需要大量的时间，并且由于其主观性，容易出现人为错误。以下工作提出了一种深度学习方法，用于检测实验性鼠模型中心肌内膜活检中组织病理学图像上的克氏锥虫无鞭毛体。从头开始实现并训练了一个 U-Net 卷积神经网络架构。实现了 99.19%的准确率和 49.43%的 Jaccard 指数。所获得的结果表明，所提出的方法可用于组织病理学图像中的无鞭毛体检测。临床意义- 该方法可以作为无鞭毛体巢的自动检测工具，可用于恰加斯病的分析和诊断。
