Abstract: To deal with the inaccurate positioning issue of the needle tip in Intracytoplasmic sperm injection microscopic images, we propose a shape-sensing-based microscopic image needle tip positioning method. In particular, the target shape and boundary information are proposed as constraints on the loss function of the image segmentation network, driving the convolutional neural network to pay more attention to the shape and boundary features of the target during the training process. The experimental results show that the performance of the proposed loss function is increased by more than 7% compared to the three commonly used baseline segmentation models. Thus, the method can be used as a plug-and-play module to improve the generalization ability of the algorithm. In addition, its localization accuracy exceeds the state-of-the-art methods in the field.

Key words: intracytoplasmicsperm injection; microscopic imaging, needle tip location, deep learning, shape perception