Quantitative analysis of immunohistochemical staining of liver slides
Summary The IHC staining intensity grading algorithm combines a series of complementary processes of deep learning and image processing in order to overcome the difficulties in cell boundary segmentation and grade definition, and provides a cell-based IHC staining intensity grading and quantification capability. The proposed method serves as a useful assistive tool for physicians in performing accurate staining quantification in tissue microscopic images.
In addition, it analyzes panoramic pathological images in order to determine the factors most strongly associated with likely disease recurrence.
This method develops an IHC auxiliary analysis system for liver pathological images, which captures the staining intensity information of each cell through a visual step-by-step process. The algorithm provides accurate quantitative information of cell grade staining for doctors’ reference and also can visualize the outcome of each step for doctors' inspection, thereby improving the credibility of the results. In addition, statistical analysis techniques are employed to highlight the most important factors associated with disease recurrence and formulate corresponding prediction equations.
At present, the statistics of the staining parameters of stained sections are determined manually by pathologists under the microscope. However, this is not only time-consuming and tedious, but also subjective and dependent to a large extent on the pathologist's experience. Therefore, we proposed herein realizes an automated IHC auxiliary analysis system, which can grade the expression of the staining sections, provide more accurate diagnosis and analysis of hepatitis, reduce the burden on doctors, and assist doctors to implement accurate medical diagnosis and postoperative treatment planning.
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Keyword Labor saving automation Interdisciplinary integration
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