  3-D pose estimation of instruments is a crucial step towards automatic scene
understanding in robotic minimally invasive surgery. Although robotic systems
can potentially directly provide joint values, this information is not commonly
exploited inside the operating room, due to its possible unreliability, limited
access and the time-consuming calibration required, especially for continuum
robots. For this reason, standard approaches for 3-D pose estimation involve
the use of external tracking systems. Recently, image-based methods have
emerged as promising, non-invasive alternatives. While many image-based
approaches in the literature have shown accurate results, they generally
require either a complex iterative optimization for each processed image,
making them unsuitable for real-time applications, or a large number of
manually-annotated images for efficient learning. In this paper we propose a
self-supervised image-based method, exploiting, at training time only, the
imprecise kinematic information provided by the robot. In order to avoid
introducing time-consuming manual annotations, the problem is formulated as an
auto-encoder, smartly bottlenecked by the presence of a physical model of the
robotic instruments and surgical camera, forcing a separation between image
background and kinematic content. Validation of the method was performed on
semi-synthetic, phantom and in-vivo datasets, obtained using a flexible
robotized endoscope, showing promising results for real-time image-based 3-D
pose estimation of surgical instruments.
