CholecTrack20: A Multi-Perspective Tracking Dataset for Surgical Tools
This addresses the problem of developing robust AI-driven surgical assistance systems for clinicians and researchers, but it is incremental as it focuses on dataset creation rather than algorithmic advancement.
The authors tackled the problem of limited AI applications in surgical tool tracking due to context-poor datasets by introducing CholecTrack20, a specialized multi-perspective dataset that redefines tracking formalization with three perspectives, comprising 20 surgical videos with over 35K frames and 65K labeled instances, and benchmarking showed current methods achieve less than 45% HOTA, failing clinical accuracy needs.
Tool tracking in surgical videos is essential for advancing computer-assisted interventions, such as skill assessment, safety zone estimation, and human-machine collaboration. However, the lack of context-rich datasets limits AI applications in this field. Existing datasets rely on overly generic tracking formalizations that fail to capture surgical-specific dynamics, such as tools moving out of the camera's view or exiting the body. This results in less clinically relevant trajectories and a lack of flexibility for real-world surgical applications. Methods trained on these datasets often struggle with visual challenges such as smoke, reflection, and bleeding, further exposing the limitations of current approaches. We introduce CholecTrack20, a specialized dataset for multi-class, multi-tool tracking in surgical procedures. It redefines tracking formalization with three perspectives: (i) intraoperative, (ii) intracorporeal, and (iii) visibility, enabling adaptable and clinically meaningful tool trajectories. The dataset comprises 20 full-length surgical videos, annotated at 1 fps, yielding over 35K frames and 65K labeled tool instances. Annotations include spatial location, category, identity, operator, phase, and scene visual challenge. Benchmarking state-of-the-art methods on CholecTrack20 reveals significant performance gaps, with current approaches (< 45\% HOTA) failing to meet the accuracy required for clinical translation. These findings motivate the need for advanced and intuitive tracking algorithms and establish CholecTrack20 as a foundation for developing robust AI-driven surgical assistance systems.