Embryo Morphological Atlas Generation via TUNETr Segmentation

The system segments the complex image analysis task into distinct functional modules: a UNet Transformer (TUNETr) for membrane segmentation, a Swin Transformer Convolution Encoder Module for feature extraction, and a nucleus lineage tracing component. This modular segmentation allows each component to specialize in specific aspects of the analysis, improving overall efficiency while managing complexity through structured organization.

The patent introduces a topology-constraint loss function as an intermediary mechanism that mediates between the deep learning model's predictions and the topological requirements of cellular structures. This loss function acts as a bridge, guiding the segmentation process to produce biologically accurate results while simplifying the overall system by providing a clear constraint framework.

The system implements self-service through automated membrane segmentation using the UNet Transformer (TUNETr) model, which processes 3D images independently without requiring manual intervention. The model is pre-trained on annotated images representing various embryonic developmental stages, enabling it to autonomously perform accurate segmentation while significantly reducing the time required compared to manual visual identification.

The patent employs synthetic pseudo GT (ground truth) images as copies or representations of actual cellular structures. These synthetic images are generated by manually adjusting annotated images and using human-in-the-loop prompts to create training data that replicates the complexity of real biological structures, allowing the deep learning model to learn accurate segmentation patterns without requiring manual analysis of every image.

The system employs parameter changes through the topology-constraint loss function, which modifies the optimization parameters during training to enforce topological constraints on segmented cellular structures. This allows the model to maintain ease of operation with automated processing while achieving high segmentation precision by adjusting parameters to account for complex cellular changes, division, and morphogenesis.

The patent combines multiple processing components into a composite system: the UNet Transformer (TUNETr) for initial segmentation, the Swin Transformer Convolution Encoder Module for feature enhancement, and the topology-constraint loss function for refinement. This composite approach integrates the strengths of different methods, maintaining operational simplicity while achieving precision suitable for complex biological processes.

The system extracts and processes only the essential information from the large volume of 4D imaging data through automated deep learning segmentation. The UNet Transformer (TUNETr) model extracts membrane boundaries and cellular structures from the volumetric imaging data, separating the critical morphological information from the redundant data, thereby maintaining comprehensive data coverage while eliminating the tedious visual identification process.

The patent replaces the mechanical process of manual visual identification with an automated deep learning-based system. The UNet Transformer (TUNETr) and Swin Transformer Convolution Encoder Module substitute human visual analysis with computational algorithms that process 4D imaging data automatically, maintaining comprehensive morphological analysis while dramatically improving productivity and eliminating manual intervention.