Magic cut segmentation
Add or remove free drawing dots or lines from masks.
One click to separate linked masks.
Adjust straight, curve, free draw lines of the desired body parts from masks.
Magic cut segmentation
Automatically segment borderlines of the body parts by drawing 2 or 3 lines on a single slice.
Feature values based on pyradiomics is automatically calculated and extracted. The results are displayed on the Worklist and exported in bulk upon request.
Labeling for AI learning
Generate AI research in JSON, NIFTI, and NRRD format, making AI research convenient.
Dataset for AI learning can be used for Radiomics research, and aview supports labeling data from other software tools.
Create different images from raw imaging data by selecting render types.
Refer to the following research papers
These contents represent summaries of scientific
publications and are unrelated to any form of advertising.
This study validates a simplified sequential scoring method called the modified length-based grade to evaluate the severity of coronary calcium (CAC) on non-ECGated chest CT.
CAC severity was independently assessed/classified using two scoring methods (visual assessment and modified length-based rating) by six radiologists and the CAC category of cardiac CT evaluated using Agatston scores was used as a reference standard.
The modified length-based rating showed higher interobserver concordance and cardiac CT than visual assessment and was effective in evaluating CAC on non-cardiogram chest CT.
Kim SY, Suh YJ, Kim NY, Lee S, Nam K, Kim J, Kim H, Lee H, Han K, Yong HS. A Modified Length-Based Grading Method for Assessing Coronary Artery Calcium Severity on Non-Electrocardiogram-Gated Chest Computed Tomography: A Multiple-Observer Study. Korean J Radiol. 2023 Apr;24(4):284-293. doi: 10.3348/kjr.2022.0826. PMID: 36996903; PMCID: PMC10067688.
Based on conical computed tomography images of the mandible, we introduce a radiological approach for legal age classification.
This study aims to identify age-related radioactive features and develop age classification models. CBCT images were analyzed from 85 subjects and 127 radiographic features were extracted using aview Research.
The 91-year-old age classification model achieved the highest accuracy, demonstrating the potential of radioactive features as imaging biomarkers for age estimation.
Jeon KJ, Kim YH, Choi H, Ha EG, Jeong H, Han SS. Radiomics approach to the condylar head for legal age classification using cone-beam computed tomography: A pilot study. PLoS One. 2023 Jan 19;18(1):e0280523. doi: 10.1371/journal.pone.0280523. PMID: 36656878; PMCID: PMC9851527.