Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Purpose: Organ delineation is one of the most tedious and time-consuming parts of radiotherapy planning. It is usually performed by manual contouring in two-dimensional slices using simple drawing tools, and it may take several hours to delineate all structures of interest in a three-dimensional (3D) data set used for planning. In this paper, a 3D model-based approach to automated organ delineation is introduced that allows for a significant reduction of the time required for contouring.
Methods And Materials: The presented method is based on an adaptation of 3D deformable surface models to the boundaries of the anatomic structures of interest. The adaptation is based on a tradeoff between deformations of the model induced by its attraction to certain image features and the shape integrity of the model. To make the concept clinically feasible, interactive tools are introduced that allow quick correction in problematic areas in which the automated model adaptation may fail. A feasibility study with 40 clinical data sets was done for the male pelvic area, in which the risk organs (bladder, rectum, and femoral heads) were segmented by automatically adapting the corresponding organ models.
Results: In several cases of the validation study, minor user interaction was required. Nevertheless, a statistically significant reduction in the time required compared with manual organ contouring was achieved. The results of the validation study showed that the presented model-based approach is accurate (1.0-1.7 mm mean error) for the tested anatomic structures.
Conclusion: A framework for organ delineation in radiotherapy planning is presented, including automated 3D model-based segmentation, as well as tools for interactive corrections. We demonstrated that the proposed approach is significantly more efficient than manual contouring in two-dimensional slices.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.ijrobp.2004.06.004 | DOI Listing |
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!