Neuron Image Segmentation via Dynamic Threshold Scoring

The patent divides the single threshold filtering process into multiple segmentation stages with different thresholds. Instead of using one intermediate threshold, the method applies a series of thresholds from low to high, creating multiple filtered versions of the image. Each threshold captures different signal intensity ranges, ensuring that important voxels with weak signals are not lost while still maintaining processing efficiency through automated multi-stage filtering.

The patent changes the threshold parameter dynamically across multiple filtering stages. By varying the signal intensity threshold from low to high across different filtering passes, the system adapts to capture voxels with different signal strengths. This parameter change strategy allows important weak-signal voxels to be preserved in early stages while stronger signals are captured in later stages, resolving the contradiction between speed and accuracy.

The patent performs preliminary actions during image acquisition by capturing a single three-dimensional image with optimized imaging parameters. Instead of requiring multiple sequential captures, the method prepares the imaging process in advance with appropriate settings that maximize signal quality in a single pass, thereby stabilizing the signal without the time cost and tissue damage associated with repeated imaging.

The patent creates multiple virtual copies of the single acquired image through computational processing. By applying different threshold filters to the same original image data, the system generates multiple filtered versions that simulate the effect of multiple captures with different exposure levels, achieving high-dynamic-range results without actually capturing multiple physical images.

The patent implements self-service automation where the segmentation system performs its own optimization through automated threshold selection and multi-stage filtering. The algorithm automatically identifies appropriate thresholds and applies filtering without human intervention, achieving accuracy comparable to manual segmentation while eliminating labor intensity. The system serves itself by using the image data to determine optimal processing parameters.

The patent replaces the mechanical manual segmentation process with an automated computational system. Instead of manual inspection and tracing by researchers, the invention uses algorithmic image processing with automated threshold-based filtering to extract neuron structures, substituting human labor with machine-based image analysis that maintains accuracy while dramatically improving processing efficiency.