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Tanqiu Liu 2017-09-18 22:20:33 -05:00 committed by GitHub
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@ -17,11 +17,11 @@ The detection process consists of 2 steps: 1) locating cell centers with a convo
A convolutional neural network model was developed to automatically identify the location of the cell centers. The pipeline employed a sliding window approach for detection. That is , a small window slides across the entire image and for position output whether the position is the center of a cell or not. A convolutional neural network model was developed to automatically identify the location of the cell centers. The pipeline employed a sliding window approach for detection. That is , a small window slides across the entire image and for position output whether the position is the center of a cell or not.
The training data was extracted and transformed from raw .csg files which are manually labeled by former researchers. Positive examples are obtained by generating a window centered on the center of the cell. Negative examples are obtained by random sampling outside the cells. The training data was extracted and transformed from raw .csg files which are manually labeled by former researchers. Positive examples are obtained by generating a window centered on the center of the cell. Negative examples are obtained by random sampling outside the cells.
<img src="./markdown/WX20170918-205841@2x.png" alt="training examples" align=center> <img src="./markdown/WX20170918-205841@2x.png" width = "400" height = "300" alt="training examples" align=center>
The convolutional neural network consists of three convolutional layers and two dense layers. The CNN predicts whether the current window is a cell. The convolutional neural network consists of three convolutional layers and two dense layers. The CNN predicts whether the current window is a cell.
<img src="./markdown/WX20170918-205912@2x.png" alt="CNN structure" align=center> <img src="./markdown/WX20170918-205912@2x.png" width = "500" height = "300" alt="CNN structure" align=center>
For each image, the sliding window method predicts a set of points which are candidates of centers of cells. Following postprocessing of these candidates includes discard points outside the cells and merge points in the cells. Finally we get precise and robust predictions of centers of cells. For each image, the sliding window method predicts a set of points which are candidates of centers of cells. Following postprocessing of these candidates includes discard points outside the cells and merge points in the cells. Finally we get precise and robust predictions of centers of cells.
@ -40,4 +40,4 @@ The principal algorithm is :
Sample results: Sample results:
<img src="./markdown/figure_1.png" width = "256" height = "256">      <img src="./markdown/figure_0.png" width = "256" height = "256"> <img src="./markdown/figure_1.png" width = "400" height = "300">      <img src="./markdown/figure_0.png" width = "400" height = "300">