ISINET: AN INSTANCE-BASED APPROACH FOR SURGICAL INSTRUMENT SEGMENTATION

C. GONZÁLEZ*, L. BRAVO-SÁNCHEZ* AND P. ARBELÁEZ

23RD INTERNATIONAL MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION (MICCAI), 2020

Abstract

We study the task of semantic segmentation of surgical instruments in robotic-assisted surgery scenes. We propose the Instance-based Surgical Instrument Segmentation Network (ISINet), a method that addresses this task from an instance-based segmentation perspective. Our method includes a temporal consistency module that takes into account the previously overlooked and inherent temporal information of the problem. We validate our approach on the existing benchmark for the task, the Endoscopic Vision 2017 Robotic Instrument Segmentation Dataset, and on the 2018 version of the dataset, whose annotations we extended for the fine-grained version of instrument segmentation. Our results show that ISINet significantly outperforms state-of-the-art methods, with our baseline version duplicating the Intersection over Union (IoU) of previous methods and our complete model triplicating the IoU.

CONTACT

Addrs. Cra. 1 E No. 19A - 40. Mario Laserna Building - School of Engineering, Bogotá, Colombia, Zip 111711, Ph. +(571) 332 4327, 332 4328, 332 4329

Universidad de los Andes | Monitored by Mineducación
Recognition as University: Decree 1297 of May 30th, 1964.
Recognition as legal entity: Resolution 28 of February 23, 1949 Minjusticia.

© Universidad de los Andes. All rights reserved.