Muallal Firas, Schöll Simon, Sommerfeldt Björn, Maier Andreas, Steidl Stefan, Buchholz Rainer, Hornegger Joachim
Med Image Comput Comput Assist Interv. 2014;17(Pt 3):377-84. doi: 10.1007/978-3-319-10443-0_48.
We propose a novel unstained cell detection algorithm based on unsupervised learning. The algorithm utilizes the scale invariant feature transform (SIFT), a self-labeling algorithm, and two clustering steps in order to achieve high performance in terms of time and detection accuracy. Unstained cell imaging is dominated by phase contrast and bright field microscopy. Therefore, the algorithm was assessed on images acquired using these two modalities. Five cell lines having in total 37 images and 7250 cells were considered for the evaluation: CHO, L929, Sf21, HeLa, and Bovine cells. The obtained F-measures were between 85.1 and 89.5. Compared to the state-of-the-art, the algorithm achieves very close F-measure to the supervised approaches in much less time.
我们提出了一种基于无监督学习的新型无染色细胞检测算法。该算法利用尺度不变特征变换（SIFT）、一种自标记算法和两个聚类步骤，以便在时间和检测精度方面实现高性能。无染色细胞成像主要由相差显微镜和明场显微镜主导。因此，该算法在使用这两种模式获取的图像上进行了评估。评估考虑了总共37张图像和7250个细胞的五种细胞系：CHO、L929、Sf21、HeLa和牛细胞。获得的F值在85.1至89.5之间。与现有技术相比，该算法在更短的时间内实现了与监督方法非常接近的F值。
