Details

Autor(en) / Beteiligte

• Laina, Iro
• Rieke, Nicola
• Rupprecht, Christian
• Vizcaíno, Josué Page
• Eslami, Abouzar
• Tombari, Federico
• Navab, Nassir
• Descoteaux, Maxime
• Franz, Alfred
• Maier-Hein, Lena
• Jannin, Pierre
• Collins, D. Louis
• Duchesne, Simon

Titel

Concurrent Segmentation and Localization for Tracking of Surgical Instruments

Ist Teil von

• Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017, p.664-672

Ort / Verlag

Cham: Springer International Publishing

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.