Automated Necrotic Tissue Masking in Multiplex Immunofluorescence Images
Solution Overview
Problem
Manual annotation of necrotic regions in multiplex immunofluorescence images is error-prone and time-consuming due to variations in size, shape, and number, leading to inaccurate detection and segmentation of target regions.
Innovation Solution
A machine-learning model, such as a U-Net model, is trained to predict necrotic tissue regions in multiplex immunofluorescence images, using preprocessing techniques like gamma correction and normalization, and generates a mask to automate the identification and segmentation of necrotic tissue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation of necrotic regions is performed, then pathologists can identify and exclude necrotic areas from analysis, but the process becomes time-consuming and error-prone due to variations in necrotic region characteristics
Solution Approach 1:
The patent replaces the manual mechanical annotation process with an automated machine learning-based image analysis system. The system uses trained models to automatically identify and segment necrotic regions in multiplex immunofluorescence images, eliminating the need for pathologists to manually annotate each region while maintaining or improving accuracy through consistent application of learned patterns across diverse necrotic morphologies
Solution Approach 2:
The system enables the images themselves to 'identify' necrotic regions through automated algorithmic analysis. The machine learning models process the images independently, automatically detecting necrotic patterns and generating segmentations without requiring human intervention for each annotation task, thereby freeing pathologists from time-consuming manual work
2Reliability
If manual annotation of necrotic regions is performed, then necrotic areas can be excluded from phenotype detection, but the process is error-prone due to variations in size, shape, and number of necrotic regions
Solution Approach 1:
The patent replaces complex manual annotation procedures with automated machine learning-based image analysis. The system consistently applies learned patterns to identify necrotic regions regardless of their varying sizes, shapes, and numbers, eliminating human error and inconsistency while managing computational complexity through efficient algorithm design and hardware acceleration
3Productivity
If automated machine learning methods are used to identify necrotic regions, then processing speed and consistency are improved, but implementation complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on extensive datasets of necrotic regions before deployment. This upfront training phase captures diverse necrotic patterns and morphologies, enabling the models to rapidly and accurately identify necrotic regions in new images without requiring complex real-time decision-making, thereby achieving high productivity with manageable implementation complexity
Data Source
AI summary
Embodiments disclosed herein generally relate to identifying necrotic tissue in a multiplex immunofluorescence image of a slice of specimen. Particularly, aspects of the present disclosure are directed to accessing a multiplex immunofluorescence image of a slice of specimen comprising a first channel for a nuclei marker and a second channel for an epithelial tumor marker, wherein the slice of specimen comprises one or more necrotic tissue regions; providing the multiplex immunofluorescence image to a machine-learning model; receiving an output of the machine-learning model corresponding to a prediction that the multiplex immunofluorescence image includes one or more necrotic tissue regions at one or more particular portions of the multiplex immunofluorescence image; generating a mask for subsequent image processing of the multiplex immunofluorescence image based on the output of the machine-learning model; and outputting the mask for the subsequent image processing.