DSpace-CRIS

Now showing 1 - 2 of 2

Some of the metrics are blocked by your
consent settings
SCTFlow: 3D MRI-to-sCT with Conditional Rectified Flow
(2026-01-01)
Rajborirug, Pharuj
;
Tungjitkusolmun, Supan
;
Gulyanon, Sarun
MRI and CT images are both crucial for radiotherapy planning, since MRI provides superior soft-tissue contrast for tumor delineation, while CT provides Hounsfield units (HU) required for dose calculation. MR-only radiotherapy offers important advantages, including reduced registration errors, elimination of additional radiation exposure, and streamlined clinical workflows. Generating synthetic CT (sCT) from MRI remains challenging due to the need for realistic HU reconstruction and the high computational demands of processing large 3D image volumes.We propose sCTFlow, a Conditional Rectified Flow (CRF) framework for 3D MRI-to-sCT translation. Unlike diffusion probabilistic models (DDPMs), sCTFlow learns a deterministic velocity field mapping noise to data, ensuring stability and requiring fewer sampling steps. Our architecture, a 3D Attention U-Net, conditions on MRI and organ segmentation via feature-wise linear modulation to predict velocity fields, which are subsequently converted into HU estimates.We evaluated our approach on the SynthRAD2023 dataset. sCTFlow achieves an MAE of 81.18 +- 19.48 HU, PSNR of 26.93, and SSIM of 0.830. We also investigated our method and found that the model captures HU distributions rather than relying on simple intensity transformations, indicating its capacity to model underlying CT characteristics. These findings demonstrate that sCTFlow has potential for reliable and clinically applicable MR-only radiotherapy workflows.
Some of the metrics are blocked by your
consent settings
Optimizing colorectal polyp detection and localization: Impact of RGB color adjustment on CNN performance
(2025-06-01)
Jamrasnarodom, Jirakorn
;
Rajborirug, Pharuj
;
Pisespongsa, Pises
;
Pasupa, Kitsuchart
Colorectal cancer, arising from adenomatous polyps, is a leading cause of cancer-related mortality, making early detection and removal crucial for preventing cancer progression. Machine learning is increasingly used to enhance polyp detection during colonoscopy, the gold standard for colorectal cancer screening, despite its operator-dependent miss rates. This study explores the impact of RGB color adjustment on Convolutional Neural Network (CNN) models for improving polyp detection and localization in colonoscopic images. Using datasets from Harvard Dataverse for training and internal validation, and LDPolypVideo-Benchmark for external validation, RGB color adjustments were applied, and YOLOv8s was used to develop models. Bayesian optimization identified the best RGB adjustments, with performance assessed using mean average precision (mAP) and F<inf>1</inf>-scores. Results showed that RGB adjustment with 1.0 R-1.0 G-0.8 B improved polyp detection, achieving an mAP of 0.777 and an F<inf>1</inf>-score of 0.720 on internal test sets, and localization performance with an F<inf>1</inf>-score of 0.883 on adjusted images. External validation showed improvement but with a lower F<inf>1</inf>-score of 0.556. While RGB adjustments improved performance in our study, their generalizability to diverse datasets and clinical settings has yet to be validated. Thus, although RGB color adjustment enhances CNN model performance for detecting and localizing colorectal polyps, further research is needed to verify these improvements across diverse datasets and clinical settings. • RGB Color Adjustment: Applied RGB color adjustments to colonoscopic images to enhance the performance of Convolutional Neural Network (CNN) models. • Model Development: Used YOLOv8s for polyp detection and localization, with Bayesian optimization to identify the best RGB adjustments. • Performance Evaluation: Assessed model performance using mAP and F<inf>1</inf>-scores on both internal and external validation datasets.

KMITL

Browse

Filters

Settings

Sort By

Results per page

Search Results