Republic of Korea
United States of America
Head & Neck
· The brain stem
· Submandibular gland
· Spinal cord
· The inner ear
· Lymph node level 1a
· Lymph node level 1b
· The mandible
· Lymph node level 2
· Optic chiasm
· Lymph node level 3
· Optic nerve
· Lymph node level 4a
· Oral cavity
· Lymph node level 4b
· The parotid gland
· Lymph node level 5
· Internal mammary lymph nodes
· Axillary lymph nodes Level 1
· Axillary lymph nodes Level 2
· Axillary lymph nodes Level 3
· Spinal cord
· Supraclavicular Lymph node (ESTRO)
· Supraclavicular Lymph node (RTOG)
· Cauda equina
· Bowel bag
· Seminal vesicle
· Penile bulb
· Spinal cord
Increase the accuracy of contouring and
reduce the time spent.
Reduced the average contouring time
Byun HK, Chang JS, Choi MS, Chun J, Jung J, Jeong C, Kim JS, Chang Y, Chung SY, Lee S, Kim YB. Evaluation of deep learning-based autosegmentation in breast cancer radiotherapy.
Radiat Oncol. 2021 Oct 14;16(1):203
Refer to the following research papers
These contents represent summaries of scientific
publications and are unrelated to any form of advertising.
This study evaluates the performance of aview RT-ACS in Coreline Soft, a deep learning-based automatic segmentation system that depicts at-risk organs (OARs) during breast radiotherapy.
The results showed that the accuracy of the automatic segmentation system is similar to that of manual segmentation by experts, with an average DSC value of 0.90.
The aview RT-ACS system has demonstrated good user satisfaction, highlighting the potential to improve the quality of breast radiotherapy and reducing variability among readers.
Byun HK, Chang JS, Choi MS, Chun J, Jung J, Jeong C, Kim JS, Chang Y, Chung SY, Lee S, Kim YB. Evaluation of deep learning-based autosegmentation in breast cancer radiotherapy. Radiat Oncol. 2021 Oct 14;16(1):203
This study evaluates the aview RT-ACS performance of coreline softs by comparing deep learning-based automatic segmentation contours of at-risk organs (OARs) in breast cancer patients receiving radiation therapy with manually depicted contours.
We demonstrated a high correlation with manual contours, achieving a Dice similarity coefficient of 0.80 or higher on all OARs and a Dice similarity coefficient of 0.70 or higher on all breast and local lymph node CTVs.
The study concluded that automatic segmentation using aview RT-ACS is feasible and beneficial for breast radiotherapy planning.
Chung SY, Chang JS, Choi MS, Chang Y, Choi BS, Chun J, Keum KC, Kim JS, Kim YB. Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery. Radiat Oncol. 2021 Feb 25;16(1):44.