MATLAB Simulation, and FPGA Implementation of the DRLSE Segmentation Algorithm
Bibliographic record
Abstract
This work focuses on using level set curves for medical image segmentation through the DRLSE (Distance Regularization Level Set Evolution) algorithm, recognized for its effectiveness and adaptability.Traditional systems face limitations in computation time and efficiency when implementing this algorithm.To overcome these challenges, FPGA (Field-Programmable Gate Arrays) are used for their parallelism and low resource consumption.The objective is to optimize medical image segmentation by implementing the DRLSE algorithm on FPGA while ensuring efficient resource and computation time management.The Algorithm was first simulated in MATLAB and tested on a database of brain, breast, and other medical images, demonstrating its robustness and flexibility.The results validate the effectiveness of the DRLSE algorithm and highlight the advantages of the FPGA in terms of speed and precision.Despite the limited documentation on implementing DRLSE on FPGA Our approach is distinguished by the use of DDR memory, which provides increased capacity to overcome the limitations of BRAM memory.Parameter optimization ensures better performance and efficient management of hardware resources.This work underscores the potential of FPGA-based implementations for accelerating computationally intensive tasks like medical image segmentation while maintaining high accuracy and efficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".