Concurrent segmentation and localization for tracking of surgical instruments

Iro Laina, Nicola Rieke, Christian Rupprecht, Josué Page Vizcaíno, Abouzar Eslami, Federico Tombari, Nassir Navab

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

115 Scopus citations

Abstract

Real-time instrument tracking is a crucial requirement for various computer-assisted interventions. To overcome problems such as specular reflection and motion blur, we propose a novel method that takes advantage of the interdependency between localization and segmentation of the surgical tool. In particular, we reformulate the 2D pose estimation as a heatmap regression and thereby enable a robust, concurrent regression of both tasks via deep learning. Throughout experimental results, we demonstrate that this modeling leads to a significantly better performance than directly regressing the tool position and that our method outperforms the state-of-the-art on a Retinal Microsurgery benchmark and the MICCAI EndoVis Challenge 2015.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
EditorsPierre Jannin, Simon Duchesne, Maxime Descoteaux, Alfred Franz, D. Louis Collins, Lena Maier-Hein
PublisherSpringer Verlag
Pages664-672
Number of pages9
ISBN (Print)9783319661841
DOIs
StatePublished - 2017
Event20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: 11 Sep 201713 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10434 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
Country/TerritoryCanada
CityQuebec City
Period11/09/1713/09/17

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