In the present paper, we address the problem of segmenting biological objects featuring corners. The main ingredients of our approach are automated feature-detection methods and mechanisms for introducing kinks in parametric spline snakes. We formulate a novel corner potential that enables the accurate segmentation of objects exhibiting sharp tips or acute angles. The optimization of active contours using the proposed keypoint-based energy yields robuster segmentation results and requires fewer parameters than traditional spline-snake approaches for the same task. The performance of our method is illustrated on microscopic images of two families of Rhabditidse roundworms.