Z-Stack Image Segmentation for 3D Biological Samples
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Solution Overview
Problem
Current imaging technologies face limitations in capturing three-dimensional samples due to restricted depth of field, which hinders the ability to image samples spanning volumes larger than the depth of focus, leading to out-of-focus regions and loss of information, especially when dealing with complex structures like organoids and tumor spheroids.
Innovation Solution
A method involving the acquisition of multiple images at different focal planes, followed by the application of filters to determine depth values and generate a projection image, allowing for the determination of in-focus image values and segmentation maps, effectively simulating a wider depth of field without the need for costly or bulky imaging apparatus.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a microscope with limited depth of focus is used to image three-dimensional samples, then the imaging apparatus remains simple and affordable, but the ability to simultaneously image the entire sample in focus is lost
Solution Approach 1:
The imaging process is segmented into multiple focal plane acquisitions. The system captures a series of images at different depths (z-stacks), where each image is in focus at a specific depth. This segmentation allows the limited depth of focus to be overcome by dividing the three-dimensional sampling into multiple two-dimensional slices that are later computationally combined.
Solution Approach 2:
The system transitions from two-dimensional imaging to three-dimensional imaging by adding the depth dimension. Multiple images are acquired at different z-positions, creating a z-stack that represents the sample in three dimensions. This dimensional expansion allows information from all depths to be captured and reconstructed.
2Measurement precision
If multiple images at different focal planes are acquired and processed, then in-focus projection images and segmentation maps are generated, but the processing complexity and computational requirements increase
Solution Approach 1:
The patent replaces complex optical mechanical systems (such as extended depth of field optics or multiple objectives) with computational image processing. The mechanical complexity of achieving wide depth of field through hardware is substituted with algorithmic processing of multiple focal plane images, using software-based focus measurement and projection techniques.
Solution Approach 2:
A depth map is introduced as an intermediary data structure that stores focus information for each pixel across the z-stack. This depth map serves as a mediator between the raw image data and the final projection image, enabling efficient computation of in-focus values by referencing pre-calculated depth information rather than processing all images directly.
3Length of stationary object
If conventional imaging methods are used, then the imaging system remains simple, but samples spanning volumes larger than the depth of field cannot be fully imaged
Solution Approach 1:
The system performs preliminary actions by acquiring a complete z-stack of images at multiple focal planes before any projection or analysis is performed. This preliminary data collection ensures that all information from the entire sample volume is captured in focus at some depth, preventing information loss that would occur with single-plane imaging.
Solution Approach 2:
The system changes the focal plane parameter systematically across multiple acquisitions. By varying the z-position (focal depth) parameter while keeping other imaging parameters constant, the system captures the entire sample volume in a series of in-focus slices, which are then reconstructed to provide comprehensive coverage of the three-dimensional sample.
Data Source
AI summary
Methods are provided to project depth-spanning stacks of limited depth-of-field images of a sample into a single image of the sample that can provide in-focus image information about three-dimensional contents of the image. These methods include applying filters to the stacks of images in order to identify pixels within each image that have been captured in focus. These in-focus pixels are then combined to provide the single image of the sample. Filtering of such image stacks can also allow for the determination of depth maps or other geometric information about contents of the sample. Such depth information can also be used to inform segmentation of images of the sample, e.g., by further dividing identified regions that correspond to the contents of the sample at multiple different depths.