Concurrent multimodality image segmentation by active contours for radiotherapy treatment planning.
Multimodality imaging information is regularly used now in radiotherapy treatment planning for cancer patients. The authors are investigating methods to take advantage of all the imaging information available for joint target registration and segmentation, including multimodality images or multiple image sets from the same modality. In particular, the authors have developed variational methods based on multivalued level set deformable models for simultaneous 2D or 3D segmentation of multimodality images consisting of combinations of coregistered PET, CT, or MR data sets. The combined information is integrated to define the overall biophysical structure volume. The authors demonstrate the methods on three patient data sets, including a nonsmall cell lung cancer case with PET/CT, a cervix cancer case with PET/CT, and a prostate patient case with CT and MRI. CT, PET, and MR phantom data were also used for quantitative validation of the proposed multimodality segmentation approach. The corresponding Dice similarity coefficient (DSC) was 0.90 +/- 0.02 (p < 0.0001) with an estimated target volume error of 1.28 +/- 1.23% volume. Preliminary results indicate that concurrent multimodality segmentation methods can provide a feasible and accurate framework for combining imaging data from different modalities and are potentially useful tools for the delineation of biophysical structure volumes in radiotherapy treatment planning.
El Naqa
Issam
I
Department of Radiation Oncology, School of Medicine, Washington University, St. Louis, Missouri 63110, USA. elnaqa@wustl.edu
Yang
Deshan
D
Apte
Aditya
A
Khullar
Divya
D
Mutic
Sasa
S
Zheng
Jie
J
Bradley
Jeffrey D
JD
Grigsby
Perry
P
Deasy
Joseph O
JO
D000465:Algorithms; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis / Q000000981:diagnostic imaging / Q000532:radiotherapy; D008279:Magnetic Resonance Imaging; D008297:Male; D049268:Positron-Emission Tomography; D011471:Prostatic Neoplasms / Q000175:diagnosis / Q000000981:diagnostic imaging / Q000532:radiotherapy; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D015899:Tomography, Emission-Computed, Single-Photon; D014057:Tomography, X-Ray Computed; D002583:Uterine Cervical Neoplasms / Q000175:diagnosis / Q000000981:diagnostic imaging / Q000532:radiotherapy