Automatic Instrument Localization in Laparoscopic Surgery

Abstract

This paper presents a tracking algorithm for automatic instrument localization in robotically assisted laparoscopic surgery. We present a simple and robust system that does not need the presence of artificial marks, or special colours to distinguish the instruments. So, the system enables the robot to track the usual instruments used in laparoscopic operations.Since the instruments are normally the most structured objects in laparoscopic scenes, the algorithm uses the Hough transform to detect straight lines in the scene. In order to distinguish among different instruments or other structured elements present in the scene, motion information is also used.We give in this paper a detailed description of all stages of the system.

EVA: Laparoscopic Instrument Tracking Based on Endoscopic Video Analysis for Psychomotor Skills Assessment

“…The first challenge comprises the automatic identification and segmentation of the instrument in the image to perform a two-dimensional (2D) tracking on the video image. In the literature, segmentation has been obtained based on color [19][20][21], marker [22,23], edge [24,25], and/or geometric features [26]. The second challenge requires calculating the three-dimensional (3D) coordinates based on the geometrical features of the segmented marker [22,27], instrument [28,29], or from an estimation of the insertion point [30].…”

Pose Estimation and Feature Tracking for Robot Assisted Surgery with Medical Imaging

“…These methods are based on the structure, mainly the apparent lines of the instrument [1,6], or on its frequential features [8,41]. In order to make the detection more robust and accurate, instruments can be marked with structuring markers as described before ( [1,43,22]) or frequential (color) markers ( [42,38]).…”

New approaches to online estimation of electromagnetic tracking errors for laparoscopic ultrasonography

“…For an automatic detection of the ultrasound tip, we follow approaches that already showed promising results under conditions close to real laparoscopic surgery: similar to Climent and Marés [21] and Voros et al [22] we use an edge detection filter and a Hough transformation [23] to extract edges from laparoscopic images. We also use additional information to select candidate lines belonging to the transducer edges.…”