Automatic cell counting in pathology images is challenging due to blurred boundaries, low-contrast, and overlapping between cells. In this paper, we train a convolutional neural network (CNN) to predict a two-dimensional direction field map and then use it to localize cell individuals for counting. Specifically, we define a direction field on each pixel in the cell regions (obtained by dilating the original annotation in terms of cell centers) as a two-dimensional unit vector pointing from the pixel to its corresponding cell center. Direction field for adjacent pixels in different cells have opposite directions departing from each other, while those in the same cell region have directions pointing to the same center. Such unique property is used to partition overlapped cells for localization and counting. To deal with those blurred boundaries or low contrast cells, we set the direction field of the background pixels to be zeros in the ground-truth generation. Thus, adjacent pixels belonging to cells and background will have an obvious difference in the predicted direction field. To further deal with cells of varying density and overlapping issues, we adopt geometry adaptive (varying) radius for cells of different densities in the generation of ground-truth direction field map, which guides the CNN model to separate cells of different densities and overlapping cells. Extensive experimental results on three widely used datasets ( i.e. , VGG Cell, CRCHistoPhenotype2016, and MBM datasets) demonstrate the effectiveness of the proposed approach.

中文翻译：

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使用方向场图的细胞定位和计数

由于边界模糊、对比度低和细胞之间重叠，病理图像中的自动细胞计数具有挑战性。在本文中，我们训练卷积神经网络 (CNN) 来预测二维方向场图，然后使用它来定位细胞个体以进行计数。具体来说，我们将单元区域中每个像素上的方向场（通过根据单元中心扩展原始注释获得）定义为从像素指向其相应单元中心的二维单位向量。不同单元中相邻像素的方向场具有相反的方向，彼此偏离，而同一单元区域中的相邻像素具有指向同一中心的方向。这种独特的属性用于划分重叠的单元格以进行定位和计数。为了处理那些模糊的边界或低对比度的单元格，我们在地面实况生成中将背景像素的方向场设置为零。因此，属于细胞和背景的相邻像素在预测的方向场中会有明显的差异。为了进一步处理不同密度的单元格和重叠问题，我们在生成ground-truth方向场图时对不同密度的单元格采用几何自适应（变化）半径，指导CNN模型分离不同密度的单元格和重叠单元格. 在三个广泛使用的数据集上的广泛实验结果（属于细胞和背景的相邻像素在预测的方向场中会有明显的差异。为了进一步处理不同密度的单元格和重叠问题，我们在生成ground-truth方向场图时对不同密度的单元格采用几何自适应（变化）半径，指导CNN模型分离不同密度的单元格和重叠单元格. 在三个广泛使用的数据集上的广泛实验结果（属于细胞和背景的相邻像素在预测的方向场中会有明显的差异。为了进一步处理不同密度的单元格和重叠问题，我们在生成ground-truth方向场图时对不同密度的单元格采用几何自适应（变化）半径，指导CNN模型分离不同密度的单元格和重叠单元格. 在三个广泛使用的数据集上的广泛实验结果（ 即，VGG Cell、CRCHistoPhenotype2016 和 MBM 数据集）证明了所提出方法的有效性。