Max Allan

TL;DR: The study shows that despite significant progress over the years, the lack of established surgical tool data‐sets, and reference format for performance assessment and method ranking is preventing faster improvement.

TL;DR: A probabilistic supervised classification method to detect pixels in laparoscopic images that belong to surgical tools and uses the classifier output to initialize an energy minimization algorithm for estimating the pose of a prior 3-D model of the instrument within a level set framework.

TL;DR: The results of the 2017 challenge on robotic instrument segmentation which involved 10 teams participating in binary, parts and type based segmentation of articulated da Vinci robotic instruments are presented.

TL;DR: This paper extends recent work in estimating rigid 3-D pose with silhouette and optical flow-based features to incorporate the articulated degrees-of-freedom (DOFs) of robotic instruments within a gradient-based optimization framework and demonstrates that the method is competitively accurate while relying solely on image data.

TL;DR: In this paper, the authors focus on binary instrument segmentation, where the objective is to label every pixel as instrument or background and instrument part segmentation where different semantically separate parts of the instrument are labeled.