Soma-Print Vector Matching for Living-to-Postmortem Tissue Alignment
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Solution Overview
Problem
Existing methods for aligning in vivo cellular recordings with post-mortem tissue samples are labor-intensive, operator-dependent, difficult to scale, and lack quantification, particularly due to angular mismatches and tissue anisotropy, limiting high-throughput studies in neural circuit mapping.
Innovation Solution
The TRU-FACT method employs multimodal image processing and tissue extraction techniques, using soma-prints for vector-based alignment and incorporating projection barcoding, enabling precise large-scale neural circuit mapping by maintaining the imaging plane parallel to in vivo imaging and employing curvature-matching objectives, with computational algorithms for quantified cell registration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual alignment methods are used, then alignment can be performed, but the process is labor-intensive and difficult to scale
Solution Approach 1:
The patent replaces manual mechanical alignment operations with an automated computational system that uses image processing algorithms and soma-print vector matching to automatically align in vivo and ex vivo images, eliminating labor-intensive manual procedures while enabling high-throughput processing
Solution Approach 2:
The patent creates computational representations (soma-prints) of cell neighborhoods that can be copied and compared across different imaging conditions, allowing automated matching without requiring physical manipulation of tissue samples
2Measurement precision
If manual alignment methods are used, then alignment can be achieved, but operator dependency increases and quantification becomes difficult
Solution Approach 1:
The patent replaces operator-dependent manual alignment with an automated computational algorithm that objectively calculates alignment based on soma-print vector comparisons, providing quantifiable measurement results that are independent of operator skill or judgment
Solution Approach 2:
The patent implements a feedback mechanism where the alignment algorithm iteratively refines matching by comparing soma-print vectors and calculating alignment scores, providing objective quantification of alignment quality that can be measured and optimized
3Reliability
If angular mismatches and tissue anisotropy are not addressed, then alignment can be performed quickly, but alignment fidelity decreases
Solution Approach 1:
The patent applies local quality by analyzing the specific geometric characteristics of cell neighborhoods (soma-prints) in each local region, allowing the alignment algorithm to account for local variations in tissue structure and orientation rather than applying uniform transformation across the entire image
Data Source
AI summary
Systems and methods for alignment of in vivo and ex vivo images in accordance with embodiments of the invention are illustrated. One embodiment includes a method for aligning in vivo with ex vivo captured tissue images, including obtaining a first image captured in vivo, obtaining a second image captured ex vivo, identifying cells in the first image and the second image, generating a soma-print for cells in the images, where each soma-print includes a plurality of vectors from a cell to each of its n nearest neighboring cells, computing a pair-wise soma-print score for each pairing of cells between the first plurality of cells and the second plurality of cells, identifying matched cell pairings between the first plurality of cells and the second plurality of cells based on their soma-print score, and annotating at least one of the images with the matched cell pairings.