Quantification of the volume of ascities can be an accurate predictor of clinical outcomes to certain pathological setting, e.g., cases of ovarian cancer. Due to the properties of ascities being a liquid, accurate segmentation can be quite a challenging task. In this paper, we show that by tuning nnU-Net, a model that learns the heuristics of the data, it is possible to achieve state-of-the-art segmentation performance. Our trained model, was able to achieve a segmentation Dice score of 0.7, with 0.75 precision and 0.69 recall on pathological test cases. This is a distinct improvement over current state-of-the-art.