Deep Learning Foreground Masking for Medical Image Segmentation
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Solution Overview
Problem
Existing methods for generating foreground masks in medical images, such as MRI, are either manual and costly or rely on thresholding techniques that are prone to inaccuracies due to their reliance on pixel intensity values alone.
Innovation Solution
A deep learning-based approach is employed to train a machine learning model that receives images and annotations to generate probability maps indicating the likelihood of pixels being part of the foreground, allowing for the creation of more accurate foreground masks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to generate foreground masks, then accuracy is high, but time consumption and cost increase
Solution Approach 1:
The patent uses deep learning models to copy and automate the expert annotation process. The neural network learns from manually annotated training images and generates foreground masks automatically, replicating the accuracy of manual methods without the time consumption. The model captures complex patterns and relationships that enable it to produce expert-level annotations autonomously.
Solution Approach 2:
The patent replaces manual mechanical annotation processes with automated computational systems. Instead of human experts manually segmenting images, the system uses neural networks that process images through multiple layers of computation, substituting human cognitive mechanisms with automated deep learning algorithms that achieve comparable accuracy.
2Productivity
If thresholding techniques are used to generate foreground masks, then processing speed is fast, but accuracy deteriorates due to reliance on pixel intensity alone
Solution Approach 1:
The patent transitions from one-dimensional pixel intensity thresholding to multi-dimensional feature extraction. The deep learning model processes images through convolutional layers that capture spatial patterns, texture information, and contextual relationships across multiple dimensions, enabling accurate segmentation that goes beyond simple intensity-based approaches while maintaining computational efficiency.
Solution Approach 2:
The patent combines multiple types of information processing within the neural network architecture. The model integrates pixel intensity data with spatial relationships, contextual information, and pattern recognition capabilities, creating a composite processing system that achieves both speed and accuracy by synthesizing multiple data dimensions simultaneously.
Data Source
AI summary
Systems and methods for training and deploying machine learning segmentation models to produce foreground masks of images are provided. The model may be used to generate foreground masks. A first method includes receiving images and annotations indicative of which pixels are in the foregrounds of the images, and generating, based on the images and the annotations, a model configured to receive, as input, a subject image and provide, as output, one or more probability maps indicative of foreground probabilities for pixels in the subject image. A foreground probability is indicative of a likelihood that the pixel is part of a foreground object or artifact in the image. Another method includes receiving a subject image, and generating a foreground mask for the subject image at least in part by applying a machine learning model to the subject image, the model having been generated based on disclosed training methods.