Image fusion technology had been widely applied in image guided radiotherapy (IGRT) for prostate cancer (PCa) based on the gold fiducial mark (GFM). Image fusion technology included the fusion of CT image, magnetic resonance image, and ultrasound image internally or externally. The application of image fusion technology had improved the identification accuracy of GFM and was helpful for the plan design of PCa radiotherapy. This article provided a systematic review of the application of fusion of various medical images in PCa IGRT in recent years. Among them, the application and result of image fusion technology in GFM identification and the impact on the plan design for PCa radiotherapy were emphasized. It hoped that this review could provide some theoretical reference for the deeper integration of image fusion technology with PCa IGRT.

Download full-text PDF

Source

Publication Analysis

Top Keywords

image fusion
24
fusion technology
24
image
10
review application
8
application image
8
fusion
8
prostate cancer
8
based gold
8
gold fiducial
8
plan design
8

Similar Publications

SEPO-FI: Deep-learning based software to calculate fusion index of muscle cells.

Comput Biol Med

January 2025

School of Computer Science, Chungbuk National University, Cheongju 28644, Republic of Korea. Electronic address:

The fusion index is a critical metric for quantitatively assessing the transformation of in vitro muscle cells into myotubes in the biological and medical fields. Traditional methods for calculating this index manually involve the labor-intensive counting of numerous muscle cell nuclei in images, which necessitates determining whether each nucleus is located inside or outside the myotubes, leading to significant inter-observer variation. To address these challenges, this study proposes a three-stage process that integrates the strengths of pattern recognition and deep-learning to automatically calculate the fusion index.

View Article and Find Full Text PDF

Objectives: This study aimed to investigate the accuracy of multiparametric magnetic resonance imaging (mpMRI), genetic urinary test (GUT), and prostate cancer prevention trial risk calculator version 2.0 (PCPTRC2) for the clinically significant prostate cancer (csPCa) diagnostic in biopsy-naïve patients.

Materials And Methods: In a single center study between 2021 and 2024 participants underwent prostate mpMRI, GUT, and ultrasound (US) guided biopsy.

View Article and Find Full Text PDF

A Feature-Enhanced Small Object Detection Algorithm Based on Attention Mechanism.

Sensors (Basel)

January 2025

School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.

With the rapid development of AI algorithms and computational power, object recognition based on deep learning frameworks has become a major research direction in computer vision. UAVs equipped with object detection systems are increasingly used in fields like smart transportation, disaster warning, and emergency rescue. However, due to factors such as the environment, lighting, altitude, and angle, UAV images face challenges like small object sizes, high object density, and significant background interference, making object detection tasks difficult.

View Article and Find Full Text PDF

Cross-Modal Collaboration and Robust Feature Classifier for Open-Vocabulary 3D Object Detection.

Sensors (Basel)

January 2025

The 54th Research Institute, China Electronics Technology Group Corporation, College of Signal and Information Processing, Shijiazhuang 050081, China.

The multi-sensor fusion, such as LiDAR and camera-based 3D object detection, is a key technology in autonomous driving and robotics. However, traditional 3D detection models are limited to recognizing predefined categories and struggle with unknown or novel objects. Given the complexity of real-world environments, research into open-vocabulary 3D object detection is essential.

View Article and Find Full Text PDF

Improving Industrial Quality Control: A Transfer Learning Approach to Surface Defect Detection.

Sensors (Basel)

January 2025

Centre of Mechanical Technology and Automation (TEMA), Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal.

To automate the quality control of painted surfaces of heating devices, an automatic defect detection and classification system was developed by combining deflectometry and bright light-based illumination on the image acquisition, deep learning models for the classification of non-defective (OK) and defective (NOK) surfaces that fused dual-modal information at the decision level, and an online network for information dispatching and visualization. Three decision-making algorithms were tested for implementation: a new model built and trained from scratch and transfer learning of pre-trained networks (ResNet-50 and Inception V3). The results revealed that the two illumination modes employed widened the type of defects that could be identified with this system, while maintaining its lower computational complexity by performing multi-modal fusion at the decision level.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!