StoneAnno is my first published first-authorship paper, presenting at SPIE 2022. With the long-term goal of fully-automated robotic endoscopic surgery, we built a dataset of endoscopic kidney stone removal videos and investigated U-Net, U-Net++, and DenseNet for the segmentation task. We found a U-Net++ model that consistently achieves >0.9 Dice score, with low loss, and produces realistic, convincing segmentations. Moving forward, I am implementing our model on hardware for deployment in ORs, as a part of my master’s thesis, and I helped Dr. Kavoussi submit an R21 grant in October 2021.

Although it will be published after StoneAnno, this shape analysis is my first completed research project and technically my first first-authorship, submitted to Brain. I wrote code in R and MATLAB to fit LMMs to the cortical data from T1w MRI of HD patients and then performed statistical analyses on the results using SurfStat and random field theory. We found that, with a novel method for measuring gyrification, LGI uniquely detects changes in the insula.