Benjamin Hou, Manas K. Nag, Jung-Min Lee, Christopher Koh, Ronald M. Summers
Quantification of the volume of ascites can be an accurate predictor of clinical outcomes to certain pathological setting, e.g., cases of ovarian cancer. Due to the properties of ascites 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 0.66, with 0.67 precision and 0.68 recall on pathological test cases. This is a distinct improvement over current state-of-the-art.