We present a new approach to distance based cell instance segmentation. Specifically, we design a new loss that more faithfully matches the shapes in segmentation geometry based on computational topology. This loss takes advantage of regularity of the distance maps that require learning. We test our approach using microscopy images consisting of many tissue types in human cells. The results indicate that the new formulation consistently improves the segmentation performance of commonly used network architectures, and the best result advances state-of-the-art.