Concurrent multimodality image segmentation by active contours for radiotherapy treatment planninga)

Abstract

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.

Reproducibility of 18F-FDG and 3′-Deoxy-3′-18F-Fluorothymidine PET Tumor Volume Measurements

“…In all of these applications, the repeatability and reproducibility with which functional volumes can be determined under different imaging conditions play a predominant role, allowing a level of confidence to be established in the use of such TV measurements. Volume-definition methodologies currently used in clinical practice are based on the use of manual delineation or fixed and adaptive thresholding (12)(13)(14), whereas several promising automatic algorithms have been proposed (16)(17)(18)(19). The major drawback of manual delineation is high inter-and intraobserver variability; in addition, the approach is time-consuming.…”

“…In addition, this method depends on the background ROI choice, which can in turn lead to reduced interobserver reproducibility for functionalvolume determination. A few automatic algorithms have been proposed (16)(17)(18)(19). The main difference between these algorithms and the threshold-based approaches is that the algorithms automatically estimate the parameters of interest and find the optimal regions' characteristics in a given image, without system-dependent parameters.…”

Rationale, Instrumental Accuracy, and Challenges of PET Quantification for Tumor Segmentation in Radiation Treatment Planning

“…Although the anatomical, metabolic and functional contours do not necessarily need to match, using images from different imaging imaging modalities (e.g. CT, PET, and MRI) is beneficial for tumor segmentation (El Naqa et al, 2007;Yu, Caldwell, Mah, & Mozeg, 2009;. Fig.…”

Segmentation of Lung Tumours in Positron Emission Tomography Scans: A Machine Learning Approach

“…The first broad group aims to segment the tumour by searching for some inhomogeneity throughout the PET scan. Although there are some interesting examples from this group, such as gradient-based (watershed) methods [4,5] and a multimodal generalisation of level set method [6], they are not as well established nor as frequently cited in current reviews as the methods from the second group, which aim to define the optimal threshold value of the uptake in order to segment a tumour. This second group includes approaches that define the optimal threshold as some fixed uptake value, or a fixed percentage of the maximum uptake value; other more sophisticated approaches determine the optimal threshold as the weighted sum of mean target uptake and mean background uptake, among other tecniques [7][8][9][10][11][12].…”