A GPU-Accelerated Deformable Image Registration Algorithm With Applications to Right Ventricular Segmentation
Bibliographic record
Abstract
Delineation of the cardiac right ventricle is essential in generating clinical measurements such as ejection fraction and stroke volume. Given manual segmentation on the first frame, one approach to segment right ventricle from all of the magnetic resonance images is to find point correspondence between the sequence of images. Finding the point correspondence with non-rigid transformation requires a deformable image registration algorithm, which often involves computationally expensive optimization. The central processing unit (CPU)-based implementation of point correspondence algorithm has been shown to be accurate in delineating organs from a sequence of images in recent studies. The purpose of this study is to develop computationally efficient approaches for deformable image registration. We propose a graphics processing unit (GPU) accelerated approach to improve the efficiency. The proposed approach consists of two parallelization components: Parallel compute unified device architecture (CUDA) version of the deformable registration algorithm; and the application of an image concatenation approach to further parallelize the algorithm. Three versions of the algorithm were implemented: 1) CPU; 2) GPU with only intra-image parallelization (sequential image registration); and 3) GPU with inter and intra-image parallelization (concatenated image registration). The proposed methods were evaluated over a data set of 16 subjects. CPU, GPU sequential image, and GPU concatenated image methods took an average of 113.13, 16.50, and 5.96 s to segment a sequence of 20 images, respectively. The proposed parallelization approach offered a computational performance improvement of around 19× in comparison to the CPU implementation while retaining the same level of segmentation accuracy. This paper demonstrated that the GPU computing could be utilized for improving the computational performance of a non-rigid image registration algorithm without compromising the accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".