A computerized system for segmenting lesions in head and neck CT scans was developed to assist radiologists in estimation of the response to treatment of malignant lesions. The system performs 3D segmentations based on a level set model and uses as input an approximate bounding box for the lesion of interest. In this preliminary study, CT scans from a pre-treatment exam and a post one-cycle chemotherapy exam of 13 patients containing head and neck neoplasms were used. A radiologist marked 35 temporal pairs of lesions. 13 pairs were primary site cancers and 22 pairs were metastatic lymph nodes. For all lesions, a radiologist outlined a contour on the best slice on both the pre- and post treatment scans. For the 13 primary lesion pairs, full 3D contours were also extracted by a radiologist. The average pre- and posttreatment areas on the best slices for all lesions were 4.5 and 2.1 cm2, respectively. For the 13 primary site pairs the average pre- and post-treatment primary lesions volumes were 15.4 and 6.7 cm3 respectively. The correlation between the automatic and manual estimates for the pre-to-post-treatment change in area for all 35 pairs was r=0.97, while the correlation for the percent change in area was r=0.80. The correlation for the change in volume for the 13 primary site pairs was r=0.89, while the correlation for the percent change in volume was r=0.79. The average signed percent error between the automatic and manual areas for all 70 lesions was 11.0±20.6%. The average signed percent error between the automatic and manual volumes for all 26 primary lesions was 37.8±42.1%. The preliminary results indicate that the automated segmentation system can reliably estimate tumor size change in response to treatment relative to radiologist's hand segmentation.