Gastric intestinal metaplasia (GIM) is regarded as a remarkable precursor for the development of intestinal-type stomach cancer. Goblet cell (GC) segmentation is the crucial step for assessing the degree of GIM by confocal laser endomicroscopy (CLE). However, GC segmentation by hand is difficult, unreliable, and time-consuming. Meanwhile, due to the high resolution and noise interference of CLE images, existing segmentation approaches perform poorly on this task. To tackle those issues, we collected 343 confocal laser endomicroscopy images of 62 patients from a Grade-A tertiary hospital. Each CLE image is manually annotated and then verified three times by skilled medical specialists. Then, U-Net is improved by incorporating the pixel gradient attention mechanism, which focuses on color gradient information around GC and captures color gradient features to direct feature maps in the skip connection layer. At last, the model output vector is used to calculate the possibility map and generate the final segmentation area. Compared with mainstream models, our proposed GC segmentation method from CLE with an improved U-Net (GCSCLE) performs the better segmentation result when tested on our CLE dataset and achieved an IOU of 87.95% and a DICE of 86.64%. Our result shows, the performance of the GCSCLE can be compared with the manual CLE image processing in clinical settings, and it can improve segmentation accuracy and save time and costs.
Keywords: confocal laser endomicroscopy; deep learning; gastric intestinal metaplasia; goblet cell.
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