# Endo-Sim2Real: Consistency learning-based domain adaptation for instrument segmentation Source: [Endo-Sim2Real: Consistency learning-based domain adaptation for instrument segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:28:00.017Z Authors: Manish Sahu, Ronja Str\\"omsd\\"orfer, Anirban Mukhopadhyay, Stefan Zachow Published: 2020-07-22T16:18:11.000Z ## Abstract Surgical tool segmentation in endoscopic videos is an important component of computer assisted interventions systems. Recent success of image-based solutions using fully-supervised deep learning approaches can be attributed to the collection of big labeled datasets. However, the annotation of a big dataset of real videos can be prohibitively expensive and time consuming. Computer simulations could alleviate the manual labeling problem, however, models trained on simulated data do not generalize to real data. This work proposes a consistency-based framework for joint learning of simulated and real (unlabeled) endoscopic data to bridge this performance generalization issue. Empirical results on two data sets (15 videos of the Cholec80 and EndoVis'15 dataset) highlight the effectiveness of the proposed \\emph{Endo-Sim2Real} method for instrument segmentation. We compare the segmentation of the proposed approach with state-of-the-art solutions and show that our method improves segmentation both in terms of quality and quantity. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Surgical Instrument Segmentation on Endovis17 to Endovis18 1.0 (target)](): Surgical Instrument Segmentation; Endovis17 to Endovis18 1.0 (target); Scissor IoU: 64.48. ## Original sources and implementation [Original paper]() [PDF]()