Computer-assisted delineation of lung tumor regions in treatment planning CT images with PET/CT image sets based on an optimum contour selection method

摘要

Datasets consisting of planning CT and PET/CT images of six lung cancer patients (mean age: 74 years; range: 65–86 years; females: 3; males: 3; mean effective diameter of GTV: 23.8 mm; range: 17.7–29.4 mm) who had received SBRT were selected for this study. The patient characteristics are summarized in Table id="xref-table-wrap-1-1" class="xref-table" href="#T1">1. All tumors were solid type cancers. Planning CT images of the patients were acquired with breath-holding at the end of expiration to delineate the tumor region and to calculate the dose distribution for each patient with a radiation treatment-planning (RTP) system, whereas PET/CT image sets were obtained to help the treatment planners delineate the tumor region from a functional point of view. The CT scan has the advantage of providing high-resolution images with anatomical detail, but it is not good at giving functional information about tumors. In contrast, PET images reflect the functional processes in biological bodies. To utilize both functional and anatomical information in the same coordinate system, the PET images were registered with the planning CT images. Initial GTV regions were determined by thresholding the PET images by a certain percentage of the SUV_max, because brighter tumor regions in PET images indicate regions where tumor cells may be active. To register PET/CT images and planning CT images, Cai et al. performed a validation study of CT and PET lung image registration based on a chamfer-matching method, with registration errors of 2–3 mm in the transverse plane, 3–4 mm in the longitudinal direction, and about 1.5 degrees in rotation [id="xref-ref-12-1" class="xref-bibr" href="#ref-12">12]. Mattes et al. proposed a free-form deformation method, with overall errors ranging from 0 to 6 mm [id="xref-ref-13-1" class="xref-bibr" href="#ref-13">13]. Figure id="xref-fig-5-1" class="xref-fig" href="#F5">5 illustrates the discrepancies in location and shape of tumor regions between the CT and PET images after the two-step registration mentioned above. These discrepancies might be caused by respiratory motion, inhomogeneous radioactivity concentrations in the GTV regions, or the large time intervals between the PET/CT and planning CT scans. The final GTVs were segmented by applying the OCS method to the initial region as determined on the PET images. The basic concept of the OCS method is to retrospectively select a global optimum object contour from among multiple active delineations with an LSM around tumors. In the OCS method, the LSM [id="xref-ref-14-1" class="xref-bibr" href="#ref-14">14] is employed for searching for the optimum object contour in the relationship between the average speed function value on an evolving curve and the evolution time. The performance of the proposed method was evaluated using a Dice similarity coefficient (DSC) [id="xref-ref-15-1" class="xref-bibr" href="#ref-15">15]. The DSC denotes the degree of region similarity between the GTV gold standard region and the GTV region obtained by the proposed method. The DSC was calculated by the following equation: class="disp-formula" id="disp-formula-5"> class="mathjax mml-math">mml:math display="block"mml:miD/mml:mimml:miS/mml:mimml:miC/mml:mimml:mo=/mml:momml:mrow/mml:mrowmml:mstylemml:mrowmml:mfracmml:mrowmml:mn2/mml:mnmml:min/mml:mimml:mo stretchy="false"(/mml:momml:mrowmml:miT/mml:mimml:mrowmml:mo∩/mml:mo/mml:mrowmml:mo?/mml:momml:miD/mml:mi/mml:mrowmml:mo stretchy="false")/mml:mo/mml:mrowmml:mrowmml:min/mml:mimml:mo stretchy="false"(/mml:momml:miT/mml:mimml:mo stretchy="false")/mml:momml:mo+/mml:momml:min/mml:mimml:mo stretchy="false"(/mml:momml:miD/mml:mimml:mo stretchy="false")/mml:mo/mml:mrow/mml:mfrac/mml:mrowmml:mo,/mml:mo/mml:mstyle/mml:math class="mathjax-text">DSC=2n(T∩?D)n(T)+n(D), class="disp-formula-label">(5) where T is the GTV gold standard region determined by two radiation oncologists, D is the GTV region contoured by the proposed method, n(T) is the number of pixels in the region T, n(D) is the number of pixels in the region D, and n(T∩D) is the number of logical AND pixels between T and D. The DSC ranges from 0 (no overlap between T and D) to 1 (T and D