Preliminary results have shown that total compartmental fat, including visceral and subcutaneous fat, can be automatically and accurately segmented on volumetric CT.
The role of imaging in the clinical setting as well as in the drug development process is expanding rapidly. Imaging technology now exists that is capable of detecting tumor response within hours. In parallel with this advance, a new array of more targeted and specific therapies are being developed. This paradigm shift in turn demands a more sophisticated way of quantifying response. There is a need to update and modify the current response evaluation criteria in solid tumors (RECIST), which rely solely on anatomic size measurement of tumors. In addition, response assessment guidelines will need to be increasingly disease-specific. Response assessment by imaging is now intimately involved with all stages of the drug development process, from exploratory drug discovery through clinical trials, as well as in clinical use. Imaging biomarkers and surrogate endpoints have the potential to speed drug approval significantly. The major funding institutions and the pharmaceutical industry are working more and more with researchers to help maintain progress in this multidisciplinary area involving oncologists, radiologists, molecular imaging specialists, medical physicists, and computer scientists.
In patients with lymphoma, identification and quantification of the tumor extent on serial CT examinations is critical for assessing tumor response to therapy. In this paper, we present a computer method to automatically match and segment lymphomas in follow-up CT images. The method requires that target lymph nodes in baseline CT images be known. A fast, approximate alignment technique along the x, y, and axial directions is developed to provide a good initial condition for the subsequent fast free form deformation (FFD) registration of the baseline and the follow-up images. As a result of the registration, the deformed lymph node contours from the baseline images are used to automatically determine internal and external markers for the marker-controlled watershed segmentation performed in the follow-up images. We applied this automated registration and segmentation method retrospectively to 29 lymph nodes in 9 lymphoma patients treated in a clinical trial at our cancer center. A radiologist independently delineated all lymph nodes on all slices in the follow-up images and his manual contours served as the "gold standard" for evaluation of the method. Preliminary results showed that 26/29 (89.7%) lymph nodes were correctly matched; i.e., there was a geometrical overlap between the deformed lymph node from the baseline and its corresponding mass in the follow-up images. Of the matched 26 lymph nodes, 22 (84.6%) were successfully segmented; for these 22 lymph nodes, several metrics were calculated to quantify the method's performance. Among them, the average distance and the Hausdorff distance between the contours generated by the computer and those generated by the radiologist were 0.9 mm (stdev. 0.4 mm) and 3.9 mm (stdev. 2.1 mm), respectively.
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