3D Biological Sample Segmentation for Time-Lapse Cell Tracking
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Solution Overview
Problem
Existing systems fail to effectively monitor and analyze the dynamic changes in multicellular objects like embryos over time, particularly in early development stages, limiting understanding of fertilization mechanisms and cellular differentiation.
Innovation Solution
A 3D segmentation method involving image acquisition, dimension reduction, clustering, and guided segmentation of biological samples to track and classify developmental phases, using neural networks and morphological criteria to define masks for each object.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of moving object
If 3D image acquisition systems are used to observe biological samples, then the ability to view sample structure in three dimensions is improved, but the ability to track dynamic changes and cellular objects over time deteriorates due to lack of effective segmentation and analysis tools
Solution Approach 1:
The patent segments the 3D image data by dividing it into multiple 2D sectional planes that can be processed individually. Each section is segmented to identify cellular objects, and these segmentations are then integrated to reconstruct 3D information. This allows precise tracking of cellular objects across time slots while maintaining 3D observation capability.
Solution Approach 2:
The patent transforms 3D volumetric data into 2D sectional planes for processing, then reconstructs 3D information by integrating results across multiple sections. This dimensionality transformation enables efficient segmentation and tracking while preserving the ability to observe and measure 3D structures and their dynamic changes over time.
2Measurement precision
If segmentation is performed on all images to track every biological object, then the completeness of object tracking is improved, but the processing time and computational complexity deteriorate
Solution Approach 1:
The patent divides the image stack into multiple time slots based on the number of cellular objects present. By segmenting images within each time slot separately and using the temporal structure, the system achieves complete object tracking while reducing overall processing time through structured, phased analysis rather than processing all images uniformly.
Solution Approach 2:
The patent performs preliminary segmentation on representative images from each time slot to identify cellular objects before processing remaining images. This preliminary action establishes object identities and trajectories early, enabling faster processing of subsequent images while maintaining complete tracking accuracy.
3Adaptability or versatility
If multiple stacks of images are processed to cover different time slots, then the coverage of developmental phases is improved, but the device complexity and data processing burden deteriorate
Solution Approach 1:
The patent segments the complete dataset into multiple time slots, each representing a specific developmental phase with a characteristic number of cellular objects. This segmentation allows the system to handle diverse developmental phases systematically while managing data processing complexity through structured organization and phase-specific analysis.
Solution Approach 2:
The patent creates a universal processing framework that handles multiple time slots and developmental phases using the same segmentation and analysis algorithms. This multi-functional approach enables coverage of various developmental stages without proportionally increasing device complexity, as the core processing pipeline remains consistent across different phases.
Data Source
AI summary
Segmenting method, for carrying out 3D segmentation of a sample, the sample comprising at least one biological object, the sample developing over time, such that at least one biological object divides or changes shape or position over time, the method comprising:at various times, acquiring a stack of images (P(t)) of the sample;segmenting images, such as to obtain masks corresponding to each biological object;implementing a segmentation algorithm that is said to be prompted, such as to use masks obtained, for an object, in an image, to define masks, for the same object, in another image.