Objective: Accurate and prompt detection of cracked teeth plays a critical role for human oral health. The aim of this paper is to evaluate the performance of a tooth crack segmentation model (namely. FDB-DeepLabv3+) on optical microscopic images. Method: The FDB-DeepLabv3+ model proposed here improves feature learning by replacing the backbone with ResNet50. https://www.rawafricaonline.com/