Organs at Risk (OAR) Segmentation Using Machine Learning Methods, M.Sc. Thesis Sharif University of Technology ; Fatemizadeh, Emad (Supervisor) ; Arabi, Hossein (Co-Supervisor)
Abstract
For radiotherapy and removal of cancerous tissues, it is necessary to determine the location of the tumor and the vulnerable structures around the tumor before treating and irradiating the high-energy beam. To do this, the images received from the patient need to be segmented. This is usually done manually, which is not only time consuming but also very expensive.Various methods for segmenting these images are presented automatically and semi-automatically, among which methods based on machine learning and deep learning have shown much higher accuracy than other methods. Despite this superiority, these methods have problems such as high computational costs, inability to learn the shape and...
Cataloging briefOrgans at Risk (OAR) Segmentation Using Machine Learning Methods, M.Sc. Thesis Sharif University of Technology ; Fatemizadeh, Emad (Supervisor) ; Arabi, Hossein (Co-Supervisor)
Abstract
For radiotherapy and removal of cancerous tissues, it is necessary to determine the location of the tumor and the vulnerable structures around the tumor before treating and irradiating the high-energy beam. To do this, the images received from the patient need to be segmented. This is usually done manually, which is not only time consuming but also very expensive.Various methods for segmenting these images are presented automatically and semi-automatically, among which methods based on machine learning and deep learning have shown much higher accuracy than other methods. Despite this superiority, these methods have problems such as high computational costs, inability to learn the shape and...
Find in contentBookmark
|
|