AI Article Synopsis
- Nuclei detection is crucial in biomedical fields like health management and disease diagnosis, but it faces challenges due to the size and density of overlapping nuclei in microscopic images.
- The authors developed a new network structure based on the Mask RCNN model, featuring a multi-path dilated residual network to improve the segmentation and detection of small, dense cell targets.
- Experimental results demonstrate that their model outperforms existing methods in recognizing and segmenting these challenging dense small objects in two different nuclear segmentation datasets.
Article Abstract
As a typical biomedical detection task, nuclei detection has been widely used in human health management, disease diagnosis and other fields. However, the task of cell detection in microscopic images is still challenging because the nuclei are commonly small and dense with many overlapping nuclei in the images. In order to detect nuclei, the most important key step is to segment the cell targets accurately. Based on Mask RCNN model, we designed a multi-path dilated residual network, and realized a network structure to segment and detect dense small objects, and effectively solved the problem of information loss of small objects in deep neural network. The experimental results on two typical nuclear segmentation data sets show that our model has better recognition and segmentation capability for dense small targets.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6562946 | PMC |
| http://dx.doi.org/10.3390/cells8050499 | DOI Listing |
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