Surgical tool tracking has a variety of applications in different surgical scenarios. Electromagnetic (EM) tracking can be utilised for tool tracking, but the accuracy is often limited by magnetic interference. Vision-based methods have also been suggested; however, tracking robustness is limited by specular reflection, occlusions, and blurriness observed in the endoscopic image. Recently, deep learning-based methods have shown competitive performance on segmentation and tracking of surgical tools. The main bottleneck of these methods lies in acquiring a sufficient amount of pixel-wise, annotated training data, which demands substantial labour costs. To tackle this issue, the authors propose a weakly supervised method for surgical tool segmentation and tracking based on hybrid sensor systems. They first generate semantic labellings using EM tracking and laparoscopic image processing concurrently. They then train a light-weight deep segmentation network to obtain a binary segmentation mask that enables tool tracking. To the authors' knowledge, the proposed method is the first to integrate EM tracking and laparoscopic image processing for generation of training labels. They demonstrate that their framework achieves accurate, automatic tool segmentation (i.e. without any manual labelling of the surgical tool to be tracked) and robust tool tracking in laparoscopic image sequences.
Keywords: annotated training data; automatic tool segmentation; binary segmentation mask; computer vision; deep learning-based methods; electromagnetic tracking; endoscopes; image segmentation; image sequences; laparoscopic image processing; learning (artificial intelligence); light-weight deep segmentation network; medical image processing; medical robotics; neural nets; pixel-wise training data; real-time surgical tool tracking; robust tool tracking; supervised segmentation; surgery; surgical scenarios; surgical tool segmentation; tracking; tracking robustness; vision-based methods.