Dendritic Tissue Reconstruction via Automated Segmentation

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Solution Overview

Problem

The reconstruction of dendritic tissues in images is currently inefficient and unreliable, relying on manual annotation which leads to low efficiency and poor accuracy.

Innovation Solution

A method involving the use of a target segmentation model applied to original image data and reconstruction reference data to automatically segment and reconstruct dendritic tissues in images, improving efficiency and reliability.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual annotation is used for dendritic tissue reconstruction, then the reconstruction process can be performed, but the reconstruction efficiency is low and the reliability of the obtained reconstruction result is poor

Engineering Contradiction:

Improvereconstruction efficiencyVSAvoidreliability of reconstruction result

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent replaces the manual mechanical annotation process with an automated computer-based segmentation system. The segmentation model automatically processes image data to identify and reconstruct dendritic tissues, eliminating the need for manual annotation while improving both efficiency and reliability of the reconstruction results.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The segmentation model is designed to autonomously perform the reconstruction task by processing image data and generating segmentation results without human intervention. The system self-corrects and self-optimizes through iterative processing, achieving reliable reconstruction results automatically.

Inventive Principle:

Principle #25Self-service

2Productivity

If manual annotation is used for dendritic tissue reconstruction, then the reconstruction can be completed, but the reconstruction efficiency remains low

Engineering Contradiction:

Improvereconstruction efficiencyVSAvoidtime consumption for reconstruction

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces the time-consuming manual annotation process with automated computational segmentation. The segmentation model processes image data rapidly using algorithms, reducing the time required for reconstruction from hours or days of manual work to much faster automated processing.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent employs preliminary processing steps including image preprocessing and feature extraction before the main segmentation task. This prepares the data in advance to enable faster and more efficient reconstruction processing by the segmentation model.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12327358B2Method for reconstructing dendritic tissue in image, device and storage medium

Publication Date: 2025.06.10 TENCENT TECHNOLOGY (SHENZHEN) CO LTD

AI summary

This application discloses a method for reconstructing a dendritic tissue in an image performed by a computer device. The method includes: acquiring original image data corresponding to a target image of a target dendritic tissue and corresponding reconstruction reference data determined based on a local reconstruction result of the target dendritic tissue in the target image; applying a target segmentation model to the original image data and the reconstruction reference data to acquire a target segmentation result for indicating a target category of each pixel in the target image, and the target category of any pixel being used for indicating whether the pixel belongs to the target dendritic tissue or not; and reconstructing the target dendritic tissue in the target image based on the target segmentation result to obtain a complete reconstruction result of the target dendritic tissue in the target image.