Congratulations to our labmate Xiaolei Guo for successfully defending her dissertation! Defending a dissertation is the last milestone to completing a Ph.D. at the University of Florida. Xiaolei presented a deep interactive segmentation framework to address the time-consuming task of fine-scale pixel-level image annotation. Utilizing transfer learning, annotators are able to interactively fine-tune a pre-trained model with easy and partial annotations. To provide immediate feedback for real-time interaction, we adapted a UNet architecture by attaching lightweight embedding layers which leveraged a prototype learning (PL) approach to efficiently learn the data metric in the embedding space. The optimized prototypes preserved the within-class data variation, enabling effective fine-tuning. This fine-tuning was seen to improve performance even on unseen data. Xiaolei then developed an interactive system called interXRoot to support a real-world application of plant root annotation. The design of the interXRoot system was based on implications derived from an interview study with eight expert annotators. A user study was conducted to explore the interplay between user and model behavior. The results showed that the PL model with stable prediction led to more user engagement. In contrast, the regular UNet model introduced prediction variance and confusion. These findings collectively contributes to the development of more effective and user friendly interactive annotation system. It also bridge the gap between human input and AI predictions in the domain of pixel-level image annotation. Xiaolei got an offer to work for Meta as a Machine Learning Engineer after her graduation. We are excited to see more of your future achievements, Xiaolei!