Embryonic Brain Ventricle Staging via Skeleton Volume Profiles
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Solution Overview
Problem
Current imaging methods for analyzing brain ventricles in developing mouse embryos, such as MRI and high-frequency ultrasounds, face challenges in accurately and automatically segmenting images due to missing boundaries, which complicates gestational staging and mutant detection in development biology studies.
Innovation Solution
A method that extracts the skeleton of the brain ventricle region, decomposes it into five components based on a volume profile along the skeleton, and uses volume vectors to accurately stage embryos and detect mutants by comparing computed volume vectors with pre-trained mean volume vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual segmentation methods are used for brain ventricles in embryonic images, then segmentation accuracy can be maintained, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent applies preliminary action by pre-training a deep learning model on a dataset of embryonic images with manually segmented brain ventricles. This pre-trained model captures the typical patterns and variations of brain ventricle structures across different gestational stages, enabling the system to perform accurate segmentation automatically without requiring manual intervention for each new image.
2Productivity
If automated segmentation methods are implemented, then processing speed increases, but segmentation accuracy deteriorates due to missing boundaries and structural variations
Solution Approach 1:
The patent applies dynamics by designing a segmentation model that adapts to varying embryonic structures across different gestational stages. The model dynamically adjusts its segmentation approach based on the input image characteristics, handling missing boundaries and structural variations through learned patterns from diverse training data, rather than applying a static segmentation rule set.
Solution Approach 2:
The patent incorporates feedback mechanisms through the deep learning model's loss function and optimization process. During training, the model receives feedback from comparison between predicted segmentations and ground truth annotations, continuously adjusting its parameters to improve accuracy. This feedback loop enables the automated system to achieve high precision by learning from errors and refining its segmentation predictions.
3Measurement precision
If detailed manual analysis of brain ventricle structures is performed, then accurate gestational staging and mutant detection can be achieved, but the complexity and time requirement increase significantly
Solution Approach 1:
The patent applies universality by designing a multi-functional deep learning model that simultaneously performs multiple tasks: segmentation of brain ventricles, estimation of gestational age, and detection of structural abnormalities. This single model replaces multiple separate analysis tools and manual procedures, reducing overall system complexity while maintaining high accuracy across all functions through shared feature extraction and coordinated optimization.
Data Source
AI summary
A method to characterize shape variations in brain ventricles during embryonic growth in mammals, the method including extracting a brain ventricle skeleton from one or more images, calculating a volume profile for the skeleton using the extracted images, partitioning the brain ventricle based on the volume profile along the skeleton, the brain ventricle being partitioned into two lateral ventricles and a main ventricle, the main ventricle being further partitioned into three sub regions, determining volume vectors of the two lateral ventricles and the three sub regions, computing a means square error between the determined computed volume vectors and a pretrained mean volume vector of embryos during different gestational stages, and classifying the embryo to the gestational stage having the lowest mean square error. A method to characterize mutant detection in mammals, the method including acquiring one or more images, computing a volume profile directly along a path of the detected skeleton from the one or more images, aligning the volume profile against a standard profile, and evaluating the volume profile against the standard profile to detect a mutation.