Trainable Deep Active Contours for Automated Image Segmentation
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Solution Overview
Problem
Current image segmentation technologies face challenges in automating the process without user supervision, as they rely heavily on manual initialization and are incompatible with deep learning approaches, and struggle to handle multiple instances simultaneously with high accuracy.
Innovation Solution
An end-to-end trainable image segmentation framework that combines a Convolutional Neural Network (CNN) with an automatically differentiable Active Contour Model (ACM), using a single CNN to generate initialization maps and parameter maps that allow for backpropagation without user intervention, enabling the segmentation of multiple instances automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual initialization and user supervision are used in image segmentation, then segmentation accuracy can be maintained, but automation extent and productivity deteriorate
Solution Approach 1:
The system enables automated segmentation by allowing the CNN to self-initialize contours and the ACM to self-evolve segmentations without user intervention. The differentiable ACM automatically adjusts parameters and propagates gradients back through the network, making the entire pipeline self-sufficient and eliminating the need for manual initialization while maintaining segmentation accuracy.
Solution Approach 2:
The patent replaces manual mechanical initialization processes with an automated computational system. The CNN automatically generates initial contours, and the differentiable ACM computationally evolves these contours through gradient-based optimization, substituting human-operated mechanical processes with automated algorithmic processes that maintain accuracy while improving automation extent.
2Ease of operation
If traditional Active Contour Model is used, then user interaction is required for initialization, but this increases device complexity and reduces ease of operation
Solution Approach 1:
The patent merges the CNN and ACM into a unified end-to-end trainable framework. The CNN provides automated initialization capabilities, while the differentiable ACM handles contour evolution. By combining these two components into a single integrated system with unified gradient flow, the patent simplifies operation (no manual initialization needed) while managing complexity through architectural integration rather than separate manual processes.
Solution Approach 2:
The differentiable ACM acts as an intermediary between the CNN output and final segmentation results. It receives initial contours from the CNN, processes them through automated evolution steps, and produces final segmentations. This intermediary component automates the previously manual initialization step, improving ease of operation while the unified training framework manages overall system complexity.
3Extent of automation
If deep learning approaches are applied to image segmentation, then automation improves, but compatibility with traditional ACM and handling of multiple instances deteriorates
Solution Approach 1:
The unified framework achieves multi-functionality by enabling the same end-to-end system to handle both single and multiple instance segmentation tasks. The CNN processes input images to generate initial contours, and the differentiable ACM evolves these contours to identify multiple distinct instances within the same image, making the system universally applicable to various segmentation scenarios while maintaining full automation.
Solution Approach 2:
The differentiable ACM introduces dynamics by allowing contours to automatically evolve and adapt during the segmentation process. Rather than static manual initialization, the contours dynamically adjust their positions and shapes based on image features and gradient feedback, enabling the automated system to adaptively handle multiple instances with varying characteristics and improving versatility across different segmentation scenarios.
Data Source
AI summary
Systems and methods for image segmentation using neural networks and active contour methods in accordance with embodiments of the invention are illustrated. One embodiment includes a method for generating image segmentations from an input image. The method includes steps for receiving an input image, identifying a set of one or more parameter maps from the input image, identifying an initialization map from the input image, and generating an image segmentation based on the set of parameter maps and the initialization map.