Deep Learning Nucleus Segmentation for Colon Cancer Staging
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Solution Overview
Problem
Current methods in anatomic pathology lack the ability to accurately distinguish between different stages of colon cancer based on morphological features of cancer nuclei, particularly between Stage 2 and Stage 4, due to limitations in standard light microscopy techniques.
Innovation Solution
The development of a machine learning model that employs computational image analysis to extract and analyze size, shape, and texture features of cancer nuclei from digital whole slide images, utilizing deep learning models like fully-convolutional neural networks and Mask-RCNN, to differentiate between cancer stages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard light microscopy techniques are used for cancer staging, then the examination process is simple and quick, but the ability to accurately distinguish between different cancer stages is insufficient
Solution Approach 1:
The patent replaces manual visual inspection by pathologists with an automated machine learning system that uses deep neural networks to analyze histological images. The system automatically extracts morphological features from whole slide images and applies trained models to classify cancer stages, substituting human visual assessment with computational analysis to improve staging accuracy
Solution Approach 2:
The patent segments the complex task of cancer staging into multiple independent components: image preprocessing, nucleus segmentation, feature extraction (size, shape, texture), and classification. This modular approach allows each component to be optimized independently while maintaining overall system accuracy for distinguishing cancer stages
2Measurement precision
If computational image analysis with multiple features is employed, then cancer stage distinction accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing histological images to enhance quality and extract relevant features before the actual classification task. The system pre-segments nuclei and pre-calculates morphological features, so that when classification is needed, the computationally intensive work has already been partially completed, reducing real-time processing requirements
Solution Approach 2:
The patent applies local quality by focusing computational analysis only on relevant regions of the image - specifically on cell nuclei rather than the entire tissue section. The system identifies and analyzes only the nuclei within tumor regions, concentrating computational resources on the most diagnostically important areas while ignoring background tissue
Data Source
AI summary
Embodiments discussed herein facilitate determination of cancer stages based at least in part on shape, size, and/or texture features of cancer nuclei. One example embodiment is a method, comprising: accessing at least a portion of a digital whole slide image (WSI) comprising a tumor; segmenting (e.g., via a first deep learning model) the tumor on the at least the portion of the digital WSI; segmenting (e.g., via a second deep learning model) cancer nuclei in the segmented tumor; extracting one or more features from the segmented cancer nuclei; providing the one or more features extracted from the segmented cancer nuclei to a trained machine learning model; and receiving, from the machine learning model, an indication of a cancer stage of the tumor.