3D Cell Segmentation Using Prompted Masks Across Time-Lapse Stacks
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Solution Overview
Problem
Existing methods for observing and analyzing the development of multicellular objects, such as embryos, are limited by the inability to effectively visualize and analyze the evolution of cells over time in three dimensions, particularly during the morula stage where cell division and differentiation occur.
Innovation Solution
A method and device for 3D segmentation of biological samples that involves acquiring image stacks at different times, applying dimensionality reduction and classification algorithms, and guided segmentation to track cell trajectories and states, using neural networks and morphological criteria to define masks for each cell.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If 3D observation systems are used to visualize cellular structures, then the ability to observe three-dimensional configurations is improved, but the complexity of segmenting and tracking cells through time-lapse sequences increases
Solution Approach 1:
The patent applies segmentation by dividing the complex 3D segmentation task into multiple 2D segmentation steps across different focal planes. Each focal plane is processed independently to identify cell boundaries, then these 2D segmentations are integrated to construct 3D cellular representations. This approach simplifies the overall complexity by breaking down the three-dimensional problem into manageable two-dimensional components.
Solution Approach 2:
The patent transitions from 3D segmentation to 2D segmentation by processing each focal plane separately. The system acquires image stacks at multiple focal planes, segments each plane independently using 2D algorithms, and then reconstructs 3D cellular structures from these 2D slices. This dimensionality reduction simplifies the segmentation process while maintaining 3D visualization capability.
2Measurement precision
If manual segmentation methods are used for 2D images, then segmentation accuracy can be achieved, but the time required to process time-lapse sequences increases significantly
Solution Approach 1:
The patent applies preliminary action by performing 2D segmentation on individual focal planes before integrating them into 3D structures. This step-by-step approach allows the system to process each plane independently and efficiently, then combine results to achieve accurate 3D segmentation. The preliminary 2D segmentation step simplifies the overall process and reduces computational time compared to attempting direct 3D segmentation.
Solution Approach 2:
The patent segments the time-lapse processing task by handling each focal plane separately rather than processing the entire 3D volume at once. This division allows for more efficient computation while maintaining accuracy, as 2D segmentation algorithms can be applied faster and with less computational overhead than 3D algorithms.
3Loss of information
If 3D segmentation is performed on entire image stacks, then complete cellular information is captured, but computational resources and processing time increase
Solution Approach 1:
The patent segments the image stack processing by treating each focal plane as a separate 2D image for segmentation purposes. This approach captures complete cellular information across all focal planes while reducing computational burden by processing smaller 2D slices independently rather than attempting to segment the entire 3D volume simultaneously.
Solution Approach 2:
The patent converts the 3D segmentation problem into multiple 2D segmentation problems by processing each focal plane separately. This dimensionality change reduces computational requirements while maintaining information completeness, as the 3D structure is reconstructed from the integrated 2D segmentations of all focal planes.
Data Source
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AI summary
A 3D segmentation method for 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 different times, acquisition of a stack of images (P(t)) of the sample; - segmentation of images, so as to obtain masks corresponding to each biological object; - implementation of a segmentation algorithm, called prompted, so as to use masks obtained, for an object, in one image, to define masks, for the same object, in another image.