Deep Learning Cell Marking in Digital Pathology Images
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Solution Overview
Problem
Conventional methods for detecting and marking target cells are time-consuming, laborious, and lack comprehensiveness, especially when dealing with larger images in various formats, leading to reduced accuracy and reliability.
Innovation Solution
An image-based target cell marking method that involves converting images into a preset format, segmenting them into blocks, using a deep learning detection model for cell detection, and integrating the blocks to mark cells, ensuring comprehensive detection and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual segmentation on cytoplasm or cell nucleus is performed, then detection accuracy may be improved, but detection time and labor cost increase significantly
Solution Approach 1:
The patent replaces manual mechanical segmentation operations with an automated deep learning detection model. The model automatically identifies and marks target cells in digital pathology images, eliminating the need for manual cytoplasm or cell nucleus segmentation while maintaining high detection accuracy. This substitution of mechanical human labor with an intelligent system resolves the contradiction between detection accuracy and detection time.
2Productivity
If simple feature extraction algorithm is used, then processing speed is improved, but detection comprehensiveness and accuracy deteriorate for larger images
Solution Approach 1:
The patent replaces simple feature extraction algorithms with a deep learning detection model that automatically learns complex features from image data. This model maintains high processing speed while significantly improving detection comprehensiveness and accuracy for large-scale pathology images, resolving the trade-off between speed and reliability.
Solution Approach 2:
The patent changes the fundamental parameters of the detection approach by transitioning from hand-crafted simple features to deep learning-learned features. This parameter change enables the system to process large images comprehensively without sacrificing speed, as the deep learning model is optimized for efficient processing while capturing complex patterns that simple features miss.
3Productivity
If image processing is limited to specific formats, then processing efficiency is improved, but adaptability to different image formats deteriorates
Solution Approach 1:
The patent implements a universal deep learning detection model that can process multiple image formats (including but not limited to specific formats) without requiring format-specific processing pipelines. The model accepts various input formats and maintains consistent detection performance, resolving the contradiction between processing efficiency and format adaptability by creating a multi-functional system that handles diverse inputs efficiently.
Data Source
AI summary
A target cell marking method, including: determining an original image format of the original scanned image, and converting the original scanned image into a first image in a preset image format; segmenting the first image into a plurality of image blocks and recording arrangement positions of the image blocks in the first image; respectively inputting the image blocks into a preset deep learning detection model to obtain first position information of target cells in the image blocks; determining second position information of the target cells in the first image according to the first position information and the corresponding arrangement positions; integrating the image blocks according to the arrangement positions to obtain a second image, and marking the target cells in the second image; and converting the second image marked by the target cells into a third image in the original image format, and displaying the third image.