Deep Learning Tumor Segmentation in Renal Cell Carcinoma Pathology
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Solution Overview
Problem
Current research on clear cell renal cell carcinoma using pathological images primarily focuses on grading and classification, failing to accurately identify specific tumor regions, and existing methods are limited in providing complete and intelligent diagnoses.
Innovation Solution
A deep learning-based segmentation method that includes data acquisition and pre-processing, building and training of a classification network (SENet), and prediction of tumor regions, enabling the automatic segmentation of tumor regions in pathological images of clear cell renal cell carcinoma.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning-based automated segmentation is applied to entire pathological images, then tumor region identification accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The pathological image is divided into multiple small image blocks (patches) that can be processed independently by the deep learning model. This segmentation approach enables the system to handle large-scale pathological images by processing them in manageable segments, reducing memory requirements and computational complexity while maintaining accurate tumor region identification through systematic processing of all patches.
2Measurement precision
If deep learning-based automated segmentation is applied to entire pathological images, then tumor region identification accuracy is improved, but processing time increases
Solution Approach 1:
By dividing the large pathological image into smaller patches, the system can process multiple patches in parallel, significantly reducing overall processing time. This approach maintains high identification accuracy while enabling efficient computation through parallel processing architectures.
Solution Approach 2:
The system initially processes a subset of image patches to identify potential tumor regions, then focuses computational resources on refining segmentation in those specific areas. This partial processing approach reduces overall processing time while maintaining accurate tumor region identification by concentrating computational effort where most needed.
3Reliability
If automated segmentation of tumor regions is implemented, then diagnostic completeness is improved, but system complexity increases
Solution Approach 1:
The system employs an intermediary classification layer that first identifies whether each image patch contains tumor tissue before applying detailed segmentation. This two-stage approach with an intermediary classification step improves diagnostic completeness by ensuring thorough tumor region detection while reducing overall system complexity by avoiding full segmentation processing in all patches.
Data Source
AI summary
A segmentation method for tumor regions in a pathological image of clear cell renal cell carcinoma based on deep learning includes data acquisition and pre-processing, building and training of a classification network SENet and prediction of tumor regions. The present invention studies clear cell renal cell carcinoma based on pathological images, yielding results with higher reliability than judgments made based on CT or MRI images. The present invention overcomes the drawback that the previous research on clear cell renal cell carcinoma is only limited to judgment on presence by being able to visually provide the position and size of tumor regions, which is convenient for the medical profession to better study the pathogenesis and directions to the treatment of clear cell renal cell carcinoma.