Clostridioides difficile infection (C. diff) is the most common cause of death due to secondary infection in hospital patients in the United States. Detection of C. diff cells in scanning electron microscopy (SEM) images is an important task to quantify the efficacy of the under-development treatments. However, detecting C. diff cells in SEM images is a challenging problem due to the presence of inhomogeneous illumination and occlusion. An Illumination normalization pre-processing step destroys the texture and adds noise to the image. Furthermore, cells are often clustered together resulting in touching cells and occlusion. In this paper, DETCID, a deep cell detection method using adversarial training, specifically robust to inhomogeneous illumination and occlusion, is proposed. An adversarial network is developed to provide region proposals and pass the proposals to a feature extraction network. Furthermore, a modified IoU metric is developed to allow the detection of touching cells in various orientations. The results indicate that DETCID outperforms the state-of-the-art in detection of touching cells in SEM images by at least 20 percent improvement of mean average precision.

中文翻译：

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DETCID：使用深度对抗网络检测具有不均匀照明的细长接触细胞

艰难梭菌感染 (C. diff) 是美国医院患者继发感染导致死亡的最常见原因。在扫描电子显微镜 (SEM) 图像中检测 C. diff 细胞是量化未开发治疗效果的一项重要任务。然而，由于存在不均匀的照明和遮挡，在 SEM 图像中检测 C. diff 细胞是一个具有挑战性的问题。照明归一化预处理步骤会破坏纹理并为图像添加噪声。此外，细胞经常聚集在一起导致接触细胞和闭塞。在本文中，提出了一种使用对抗训练的深度细胞检测方法 DETCID，特别是对非均匀光照和遮挡具有鲁棒性。开发了一个对抗网络来提供区域提议并将提议传递给特征提取网络。此外，还开发了一种改进的 IoU 度量，以允许检测各种方向的触摸单元。结果表明，DETCID 在检测 SEM 图像中的接触细胞方面优于最先进的技术，平均精度提高了至少 20%。