Ascites generally manifests in the advance stage of ovarian cancer, and often mimicked by abdominal fluids such as urine in bladder. Segmentation of ascites in the pelvic region becomes increasingly challenging when the bladder is filled with urine. Anatomical location is utilized in this work to distinguish ascites from bladder. The location information is computed from a body part regressor and concatenated with contrast-enhanced computed tomography (CT) data through an embedding layer. A 3D residual U-Net is trained on the concatenated data to segment ascites and identify bladder simultaneously. 112 CT scans were used in this study; 55 of them were used for training, 20 for validation, and the remaining 37 for testing. Dice coefficient score and Jacard index are two metrics to evaluate ascites and bladder segmentation. In comparison with 3D residual U-Net, the addition of anatomical location information and two-class segmentation improved the average dice scores of ascites and urine segmentation from 0.44±0.23 to 0.51±0.16 and 0.36±0.15 to 0.40±0.13 respectively. The average volume errors of ascites and urine were reduced from 1.81±3.09 to 0.93±1.85 liters and 0.6±0.81 to 0.56±0.76 liters, respectively. These results suggested that anatomical location information and two-class segmentation are the key features to improve ascites segmentation by differentiating bladder filled with urine regions.