DETCID：使用深度对抗网络检测具有不均匀照明的细长触摸细胞
DETCID: Detection of Elongated Touching Cells with Inhomogeneous Illumination using a Deep Adversarial Network
摘要：
艰难梭菌感染（C. diff）是美国医院患者因继发感染而死亡的最常见原因。检测C.扫描电子显微镜（SEM）图像中的diff细胞是量化欠发达治疗的功效的重要任务。然而，检测C.由于存在不均匀照明和遮挡，SEM图像中的差异细胞是一个具有挑战性的问题。照明标准化预处理步骤破坏纹理并向图像添加噪声。此外，细胞通常聚集在一起，导致接触细胞和闭塞。本文提出了一种使用对抗训练的深度细胞检测方法DETCID，特别是对不均匀照明和遮挡具有鲁棒性。一个对抗性网络被开发来提供区域建议并将建议传递给特征提取网络。此外，改进的IoU度量被开发以允许检测在各种方向上的触摸单元。结果表明，DETCID优于国家的最先进的检测接触细胞在SEM图像中的平均平均精度至少提高了20%。
英文摘要：
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.
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