Automated 3D Cardiac Segmentation With GANs and Patch Extraction
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Solution Overview
Problem
Conventional image segmentation techniques, including deep learning methods, are limited and inaccurate in segmenting anatomical structures like the heart, particularly in 3-D cardiac images, due to lack of contextual information and inefficiency in segmenting till the last slice, leading to high intra- and interobserver variability and time-consuming manual delineation.
Innovation Solution
A Generative Adversarial Network (GAN) based architecture is proposed for 3-D image segmentation of anatomical structures, utilizing a segmentation network model with a generator and patch-based discriminator, incorporating 3-D contextual information, and employing pre-processing techniques like orientation normalization and image augmentation to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If 2-D or slice by-slice data is used for segmentation, then the training is lightweight and requires less data, but the segmentation lacks 3-D contextual information and is inaccurate till the last slice
Solution Approach 1:
The patent transitions from 2-D slice-by-slice segmentation to 3-D volumetric segmentation by processing entire 3-D cardiac images as input. This dimensional change enables the model to capture spatial contextual information across all slices simultaneously, improving segmentation accuracy particularly at the last slice while maintaining reasonable training complexity through efficient 3-D convolution operations
2Measurement precision
If manual delineation is performed, then detailed segmentation can be achieved, but it is time-consuming and tedious with high intra- and interobserver variability
Solution Approach 1:
The patent replaces manual mechanical delineation with an automated deep learning-based segmentation system. The GAN-based model automatically processes 3-D cardiac images to generate segmentations, eliminating the need for manual tracing while maintaining high precision. This substitution dramatically reduces time consumption and eliminates interobserver variability inherent in manual methods
3Device complexity
If conventional segmentation techniques are used, then the process is simpler, but they are inaccurate and inefficient in segmenting till the last slice of the image
Solution Approach 1:
The patent implements a feedback mechanism through the adversarial training process where the discriminator provides continuous feedback to the generator about segmentation accuracy. This feedback loop enables the model to learn from errors and progressively improve segmentation reliability across all slices including the last slice, while the modular architecture keeps the overall system complexity manageable
Data Source
AI summary
This disclosure relates generally to methods and systems for automated image segmentation of an anatomical structure such as heart. Most of the techniques in literature are using 2-D or slice by-slice data due to lightweight and need of less data for training. These networks lack 3-D contextual information. Further, the conventional techniques are inaccurate and inefficient in the 3-D image segmentation till the last slice of the image. The present disclosure solves automated 3-D image segmentation of the anatomical structure such as heart, by proposing a new Generative Adversarial Network (GAN) based architecture for the 3-D segmentation, with a patch-based extraction technique and a class-weighted generalized dice loss. The proposed 3-D GAN based architecture is capable of storing the 3-D contextual information for the image segmentation of the anatomical structure, with high accuracy.