Purpose: A probabilistic image segmentation algorithm called stochastic region competition is proposed for performing Doppler sonography segmentation.
Methods: The image segmentation is conducted by maximizing a posteriori that models histogram likelihood, gradient likelihood, and a spatial prior. The optimization is done by a modified expectation and maximization (EM) method that aims to improve computation efficiency and avoid local optima.
Results: The algorithm was tested on 155 color Doppler sonograms and compared with manual delineations. The qualitative assessment shows that our algorithm is able to segment mass lesions under the condition of low image quality and the interference of the color-encoded Doppler information. The quantitative assessment analysis shows that the average distance between the algorithm-generated boundaries and manual delineations is statistically comparable to the variability of manual delineations. The ratio of the overlapping area between the algorithm-generated boundaries and manual delineations is also comparable to that between different sets of manual delineations. A reproductivity test was conducted to confirm that the result is statistically reproducible.
Conclusions: The algorithm can be used to perform Doppler sonography segmentation and to replace the tedious manual delineation task in clinical application.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1118/1.4705350 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Large language model-augmented learning for auto-delineation of treatment targets in head-and-neck cancer radiotherapy.
Radiother Oncol
January 2025
Department of Radiation Oncology, Stanford University, Stanford, CA, United States. Electronic address:
Background And Purpose: Radiation therapy (RT) is highly effective, but its success depends on accurate, manual target delineation, which is time-consuming, labor-intensive, and prone to variability. Despite AI advancements in auto-contouring normal tissues, accurate RT target volume delineation remains challenging. This study presents Radformer, a novel visual language model that integrates text-rich clinical data with medical imaging for accurate automated RT target volume delineation.
View Article and Find Full Text PDFApplication of Ultrasound Radiomics in Differentiating Benign from Malignant Breast Nodules in Women with Post-Silicone Breast Augmentation.
Curr Oncol
January 2025
Department of Medical Ultrasound, West China Hospital, Sichuan University, Chengdu 610041, China.
Purpose: To evaluate the diagnostic value of ultrasound radiomics in distinguishing between benign and malignant breast nodules in women who have undergone silicone breast augmentation.
Methods: A retrospective study was conducted of 99 breast nodules detected by ultrasound in 93 women who had undergone silicone breast augmentation. The ultrasound data were collected between 1 January 2006 and 1 September 2023.
Preoperative prediction of pituitary neuroendocrine tumor invasion using multiparametric MRI radiomics.
Front Oncol
January 2025
Medical Imaging Center, The First Hospital of Kunming, Kunming, China.
Objective: The invasiveness of pituitary neuroendocrine tumor is an important basis for formulating individualized treatment plans and improving the prognosis of patients. Radiomics can predict invasiveness preoperatively. To investigate the value of multiparameter magnetic resonance imaging (mpMRI) radiomics in predicting pituitary neuroendocrine tumor invasion into the cavernous sinus (CS) before surgery.
View Article and Find Full Text PDFWhole-Heart Histological and CMR Validation of Electroanatomic Mapping by Multielectrode Catheters in an Ovine Model.
JACC Clin Electrophysiol
January 2025
Department of Cardiology, Westmead Hospital, Sydney, New South Wales, Australia; Westmead Applied Research Centre, University of Sydney, Sydney, New South Wales, Australia. Electronic address:
Background: Accurate electroanatomic mapping is critical for identifying scar and the long-term success of ventricular tachycardia ablation.
Objectives: This study sought to determine the accuracy of multielectrode mapping (MEM) catheters to identify scar on cardiac magnetic resonance (CMR) and histopathology.
Methods: In an ovine model of myocardial infarction, we examined the effect of electrode size, spacing, and mapping rhythm on scar identification compared to CMR and histopathology using 5 multielectrode mapping catheters.
MR-linac: role of artificial intelligence and automation.
Strahlenther Onkol
January 2025
Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Rämistrasse 100, 8091, Zurich, Switzerland.
The integration of artificial intelligence (AI) into radiotherapy has advanced significantly during the past 5 years, especially in terms of automating key processes like organ at risk delineation and treatment planning. These innovations have enhanced consistency, accuracy, and efficiency in clinical practice. Magnetic resonance (MR)-guided linear accelerators (MR-linacs) have greatly improved treatment accuracy and real-time plan adaptation, particularly for tumors near radiosensitive organs.
View Article and Find Full Text PDFWant 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!