Mask R-CNN Segmentation for Overlapping Crypts in Histology
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Solution Overview
Problem
Current digital image processing techniques, particularly in digital pathology, struggle with accurately segmenting individual and overlapping glands in histological images, which is crucial for assessing the severity of Inflammatory Bowel Disease (IBD), as existing models fail to capture the complexity of inflammation and response to treatment effectively.
Innovation Solution
The use of advanced Deep Neural Networks (DNNs), specifically Convolutional Neural Networks (CNNs) and Region-based CNNs, for object detection, segmentation, and characterization, including the application of Mask R-CNN for parallel object detection and segmentation, enables the precise identification and analysis of isolated and overlapping crypts in histological images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If state-of-the-art Deep Learning models use a combination of different processing units to maximize information at object boundaries, then segmentation accuracy for well-separated glands is improved, but the models fail to segment individual glands when glands are physically touching or overlapping
Solution Approach 1:
The patent applies segmentation by dividing the complex task of segmenting overlapping glands into multiple processing stages. The system first identifies individual gland regions, then processes boundaries separately, and finally resolves overlaps through iterative refinement. This multi-stage segmentation approach enables the model to handle both well-separated and overlapping glands effectively.
Solution Approach 2:
The patent introduces additional dimensional information by processing gland segmentation in multiple scales and resolutions. The system analyzes images at different magnification levels and combines the results, adding a dimensional aspect that helps distinguish overlapping glands that appear merged at standard resolution but can be separated through multi-scale analysis.
2Ease of manufacture
If existing grading systems are used to capture inflammation complexity in IBD, then standardization is achieved, but histological features of glands are largely unexploited and disease severity assessment is limited
Solution Approach 1:
The patent applies parameter changes by extracting and analyzing multiple histological parameters simultaneously, including gland size, shape, density, and architectural features. The system transforms traditional qualitative grading into quantitative measurements of numerous parameters, enabling comprehensive assessment of disease severity while maintaining standardized evaluation protocols.
Solution Approach 2:
The patent implements universality by creating a multi-functional analysis system that simultaneously performs segmentation, feature extraction, and disease severity assessment. The same computational framework handles multiple types of histological features (glandular architecture, inflammatory cells, stromal changes) within a unified system, maximizing information utilization while maintaining grading standardization.
Data Source
AI summary
Systems and methods disclosed herein relate generally to systems and methods for detection, segmentation and characterization of isolated or overlapping object instances in digital images, applicable for detection, segmentation and characterization of crypts in histological images from patients with gastrointestinal disorders.