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Comparison of 2 Atlas-Based Segmentation Methods for Head and Neck Cancer With RTOG-Defined Lymph Node Levels
S882 optimization parameters in VMAT1, additional plans were calculated as follows: VMAT2, in which the arcs were broken into sub-arcs (181 -0 , 0 -179 , 230 -0 , 0 -130 ); VMAT3, in which optimization duration was increased by running the optimization twice and VMAT4, in which duration was increased by continuing optimization from the last multiresolution level (MR4). The impact of these optimization strategies on the final dose distribution was evaluated statistically by comparing the number of monitor units and various indices of plan quality (PTV coverage, tissue protection, conformity and homogeneity indices). The gradient measure (GM) parameter, which indicates the dose slope around the target, was also evaluated. The variables that described the patients’ anatomical characteristics were also analyzed to determine their potential influence on the resulting plan. Results: The VMAT2 strategy increased the number of control points by 34% (from 178 for a full arc and 114 for 260 -arc to 98 for each of the smaller arcs), thus improving the quality of that plan. The stochastic component of the optimization process was reduced by increasing its duration (VMAT3 and VMAT4 strategies). VMAT4 yielded the best results in terms of conformity. No differences between optimization strategies were observed in any of the other indicators of plan quality. Compared to the base plan (VMAT1), both VMAT2 and VMAT3 resulted in an increased number of monitor units although the GM was preferable only for VMAT2. Both the VMAT4 and VMAT3 strategies yielded inferior GM values compared to both VMAT1 and VMAT2. A correlation was observed between the number of monitor units, the target and selected body volumes which were chosen to represent the patients’ anatomical characteristics. Conclusions: All of the strategies produced viable treatment plans, with acceptable dose-volume histograms for the target and organs at risk, as well as reasonable quality indices. The best strategy in terms of delivery time was the VMAT4 strategy. In terms of GM improvement, the best strategy was VMAT2. Author Disclosure: M. Adamczyk: None. T. Piotrowski: None.
3707 Comparison of 2 Atlas-Based Segmentation Methods for Head and Neck Cancer With RTOG-Defined Lymph Node Levels A.S. Nelson, J.W. Piper, A.R. Javorek, S.D. Pirozzi, and M. Lu; MIM Software, Inc., Beachwood, OH Purpose/Objective(s): To compare two methods of atlas-based segmentation using a head and neck cancer atlas with RTOG-defined lymph node levels. Materials/Methods: Twenty subjects with CT scans and brachial plexus, brain, brainstem, constrictors, larynx, RTOG-defined lymph node levels, mandible, orbits, parotids, spinal cavity, and spinal cord contours were used to create an atlas database. Two atlas-based segmentation methods were tested: Method 1 used a free-form intensity-based deformable registration while Method 2 used an additional automatic registration approximation method to influence the intensity-based deformation. Atlas segmentation was performed using a leave-one-out analysis (subject being tested was excluded from the atlas). The 5 best matched atlas subjects were automatically chosen and deformed to the test subject (Multi-5). Contours were combined using Majority Vote or where 3 of 5 contours overlapped. Auto contours were compared to the manually defined using the Dice Similarity Index for both methods. Results: The table shows the results for Method 1 compared to Method 2 where each structure had a higher dice score for Method 2 and were statistically significant (p < 0.05) for all structures except for the larynx which trended towards significance (p Z 0.069). Conclusions: A new method of atlas-based segmentation which uses an automatic registration approximation technique to influence the intensitybased deformation was found to be significantly more accurate than an intensity-based deformation method alone. Author Disclosure: A.S. Nelson: A. Employee; MIM Software, Inc. R. Ownership Other; MIM Software, Inc. J.W. Piper: A. Employee; MIM
International Journal of Radiation Oncology Biology Physics Scientific Abstract 3707; Table Structure Brain Brainstem Constrictors Larynx LN Levels Mandible Orbit Parotid Spinal Cord
Method 1 0.97 0.78 0.49 0.75 0.66 0.82 0.72 0.71 0.71
+/+/+/+/+/+/+/+/+/-
0.02 0.10 0.09 0.16 0.06 0.10 0.17 0.09 0.16
Dice Comparison between Atlas Methods Method 2 0.98 0.85 0.55 0.80 0.70 0.88 0.83 0.74 0.73
+/+/+/+/+/+/+/+/+/-
0.00 0.03 0.07 0.08 0.05 0.03 0.06 0.07 0.15
% Improvement 32.5 32.1 11.4 21.8 11.7 31.8 38.1 9.9 8.2
Software, Inc. R. Ownership Other; MIM Software, Inc. A.R. Javorek: A. Employee; MIM Software, Inc. S.D. Pirozzi: A. Employee; MIM Software, Inc. M. Lu: A. Employee; MIM Software, Inc.
3708 A Web-Based Image Processing and Plan Evaluation Platform (WIPPEP) for Future Cloud-Based Radiation Therapy X. Chai, L. Liu, and L. Xing; Stanford University, Palo Alto, CA Purpose/Objective(s): Visualization and processing of medical images and radiation treatment plan evaluation have traditionally been constrained to local workstations with limited computation power and ability of data sharing and software update. We present a web-based image processing and planning evaluation platform (WIPPEP) for radiation therapy applications with high efficiency, ubiquitous web access, and real-time data sharing. Materials/Methods: This software platform consists of three parts: web server, image server and computation server. Each independent server communicates with each other through HTTP requests. The web server is the key component that provides visualizations and user interface through front-end web browsers as well as relay information to the backend to process user requests. The image server serves as a PACS system that stores and manages image data. The computation server performs the actual image processing and dose calculation algorithms. This platform is built on top of existing open source technologies. We built the web server based on the open source radiology software Oviyam, and added visualization functions for radiation therapy applications. These functions include the visualization of image, contour and dose distributions in three orthogonal directions, DVH and MLC plotting and manual contouring. The web server backend is developed using Java Servlets and the frontend browser visualization component is developed using HTML5, Javascript, and jQuery. The image server is based on the open source DCME4CHEE PACS system. The computation server can be written in any programming language as long as it can send/receive HTTP requests. Our computation server was implemented in both Delphi and Python, which can both process data directly or via a C++ program DLL. Results: This software platform is running on a 32-core CPU server on 3 virtual servers hosting the web server, image server, and computation server separately. Users can now visit our internal website with a Chrome browser, select a specific patient, visualize all image and RT structures belonging to this patient and perform image segmentation running Delphi computation server and Monte Carlo dose calculation on Python computation server. We have used our platform to successfully upload DCM data of 10 patients, each with 200 Megabytes worth of RT files to our image server. The data of each patient can then be downloaded, read and displayed on web browser with an average load time of 40 seconds. Conclusions: We have developed a web-based image processing and plan evaluation platform prototype for radiation therapy. This system has clearly demonstrated the feasibility of performing image processing and plan evaluation platform through a web browser and exhibited potential for future cloud based radiation therapy. Author Disclosure: X. Chai: None. L. Liu: None. L. Xing: None.
3707 Comparison of 2 Atlas-Based Segmentation Methods for Head and Neck Cancer With RTOG-Defined Lymph Node Levels A.S. Nelson, J.W. Piper, A.R. Javorek, S.D. Pirozzi, and M. Lu; MIM Software, Inc., Beachwood, OH Purpose/Objective(s): To compare two methods of atlas-based segmentation using a head and neck cancer atlas with RTOG-defined lymph node levels. Materials/Methods: Twenty subjects with CT scans and brachial plexus, brain, brainstem, constrictors, larynx, RTOG-defined lymph node levels, mandible, orbits, parotids, spinal cavity, and spinal cord contours were used to create an atlas database. Two atlas-based segmentation methods were tested: Method 1 used a free-form intensity-based deformable registration while Method 2 used an additional automatic registration approximation method to influence the intensity-based deformation. Atlas segmentation was performed using a leave-one-out analysis (subject being tested was excluded from the atlas). The 5 best matched atlas subjects were automatically chosen and deformed to the test subject (Multi-5). Contours were combined using Majority Vote or where 3 of 5 contours overlapped. Auto contours were compared to the manually defined using the Dice Similarity Index for both methods. Results: The table shows the results for Method 1 compared to Method 2 where each structure had a higher dice score for Method 2 and were statistically significant (p < 0.05) for all structures except for the larynx which trended towards significance (p Z 0.069). Conclusions: A new method of atlas-based segmentation which uses an automatic registration approximation technique to influence the intensitybased deformation was found to be significantly more accurate than an intensity-based deformation method alone. Author Disclosure: A.S. Nelson: A. Employee; MIM Software, Inc. R. Ownership Other; MIM Software, Inc. J.W. Piper: A. Employee; MIM
International Journal of Radiation Oncology Biology Physics Scientific Abstract 3707; Table Structure Brain Brainstem Constrictors Larynx LN Levels Mandible Orbit Parotid Spinal Cord
Method 1 0.97 0.78 0.49 0.75 0.66 0.82 0.72 0.71 0.71
+/+/+/+/+/+/+/+/+/-
0.02 0.10 0.09 0.16 0.06 0.10 0.17 0.09 0.16
Dice Comparison between Atlas Methods Method 2 0.98 0.85 0.55 0.80 0.70 0.88 0.83 0.74 0.73
+/+/+/+/+/+/+/+/+/-
0.00 0.03 0.07 0.08 0.05 0.03 0.06 0.07 0.15
% Improvement 32.5 32.1 11.4 21.8 11.7 31.8 38.1 9.9 8.2
Software, Inc. R. Ownership Other; MIM Software, Inc. A.R. Javorek: A. Employee; MIM Software, Inc. S.D. Pirozzi: A. Employee; MIM Software, Inc. M. Lu: A. Employee; MIM Software, Inc.
3708 A Web-Based Image Processing and Plan Evaluation Platform (WIPPEP) for Future Cloud-Based Radiation Therapy X. Chai, L. Liu, and L. Xing; Stanford University, Palo Alto, CA Purpose/Objective(s): Visualization and processing of medical images and radiation treatment plan evaluation have traditionally been constrained to local workstations with limited computation power and ability of data sharing and software update. We present a web-based image processing and planning evaluation platform (WIPPEP) for radiation therapy applications with high efficiency, ubiquitous web access, and real-time data sharing. Materials/Methods: This software platform consists of three parts: web server, image server and computation server. Each independent server communicates with each other through HTTP requests. The web server is the key component that provides visualizations and user interface through front-end web browsers as well as relay information to the backend to process user requests. The image server serves as a PACS system that stores and manages image data. The computation server performs the actual image processing and dose calculation algorithms. This platform is built on top of existing open source technologies. We built the web server based on the open source radiology software Oviyam, and added visualization functions for radiation therapy applications. These functions include the visualization of image, contour and dose distributions in three orthogonal directions, DVH and MLC plotting and manual contouring. The web server backend is developed using Java Servlets and the frontend browser visualization component is developed using HTML5, Javascript, and jQuery. The image server is based on the open source DCME4CHEE PACS system. The computation server can be written in any programming language as long as it can send/receive HTTP requests. Our computation server was implemented in both Delphi and Python, which can both process data directly or via a C++ program DLL. Results: This software platform is running on a 32-core CPU server on 3 virtual servers hosting the web server, image server, and computation server separately. Users can now visit our internal website with a Chrome browser, select a specific patient, visualize all image and RT structures belonging to this patient and perform image segmentation running Delphi computation server and Monte Carlo dose calculation on Python computation server. We have used our platform to successfully upload DCM data of 10 patients, each with 200 Megabytes worth of RT files to our image server. The data of each patient can then be downloaded, read and displayed on web browser with an average load time of 40 seconds. Conclusions: We have developed a web-based image processing and plan evaluation platform prototype for radiation therapy. This system has clearly demonstrated the feasibility of performing image processing and plan evaluation platform through a web browser and exhibited potential for future cloud based radiation therapy. Author Disclosure: X. Chai: None. L. Liu: None. L. Xing: None.