Weakly Supervised Convolutional LSTM Approach for Tool Tracking in Laparoscopic Videos (1812.01366v2)

Abstract: Purpose: Real-time surgical tool tracking is a core component of the future intelligent operating room (OR), because it is highly instrumental to analyze and understand the surgical activities. Current methods for surgical tool tracking in videos need to be trained on data in which the spatial positions of the tools are manually annotated. Generating such training data is difficult and time-consuming. Instead, we propose to use solely binary presence annotations to train a tool tracker for laparoscopic videos. Methods: The proposed approach is composed of a CNN + Convolutional LSTM (ConvLSTM) neural network trained end-to-end, but weakly supervised on tool binary presence labels only. We use the ConvLSTM to model the temporal dependencies in the motion of the surgical tools and leverage its spatio-temporal ability to smooth the class peak activations in the localization heat maps (Lh-maps). Results: We build a baseline tracker on top of the CNN model and demonstrate that our approach based on the ConvLSTM outperforms the baseline in tool presence detection, spatial localization, and motion tracking by over 5.0%, 13.9%, and 12.6%, respectively. Conclusions: In this paper, we demonstrate that binary presence labels are sufficient for training a deep learning tracking model using our proposed method. We also show that the ConvLSTM can leverage the spatio-temporal coherence of consecutive image frames across a surgical video to improve tool presence detection, spatial localization, and motion tracking. keywords: Surgical workflow analysis, tool tracking, weak supervision, spatio-temporal coherence, ConvLSTM, endoscopic videos

• The paper presents a novel CNN-ConvLSTM architecture that tracks surgical tools with only binary presence labels.
• The method increases tool detection mAP by over 5%, improves localization accuracy by about 13.9%, and enhances MOTA by 12.6%.
• The approach paves the way for intelligent operating room systems, reducing the need for manual spatial annotations.

Analysis of "Weakly Supervised Convolutional LSTM Approach for Tool Tracking in Laparoscopic Videos"

The paper entitled "Weakly Supervised Convolutional LSTM Approach for Tool Tracking in Laparoscopic Videos" presents an advanced methodology for real-time surgical tool tracking in laparoscopic videos without relying on spatial annotations during training. The approach leverages weakly supervised learning with binary presence annotations of surgical tools, adopting a Convolutional LSTM (ConvLSTM) architecture.

Methodological Framework

The primary contribution of this research lies in the novel employment of a CNN combined with ConvLSTM to address the challenge of surgical tool tracking. The paper circumvents the need for manually intensive spatial annotations by using binary presence labels of tools, which significantly reduces the complexity and effort required for data preparation. The methodological innovation involves the integration of temporal data modeling through ConvLSTM, which enhances the detection of surgical tools by capturing spatio-temporal dependencies across consecutive frames in laparoscopic videos.

The proposed architecture consists of three configurations of ConvLSTM networks, each designed to evaluate different placements of the ConvLSTM unit within the network pipeline. The architectures, namely $\mathbb{R+C+CL}$, $\mathbb{R+CL+C}$, and $\mathbb{R+CL}$, were rigorously analyzed to determine their efficacy in tool tracking tasks.

Numerical Results and Performance Evaluation

The research demonstrates significant performance improvements over baseline CNN models across three essential tasks: tool presence detection, spatial localization, and motion tracking. Notably, the ConvLSTM-enhanced models showcased an increase in mean average precision (mAP) for tool presence detection by over 5.0%, and an improvement in spatial localization accuracy by approximately 13.9%.

For motion tracking, the proposed system was evaluated using the CLEAR MOT metrics. The advancements brought by incorporating ConvLSTM are underscored by a substantial 12.6% improvement in Multiple Object Tracking Accuracy (MOTA), indicating robust capabilities in handling the birth, tracking, and cessation of tool trajectories within video sequences. These enhancements are attributed to the ConvLSTM's ability to refine the class peak activations, which likely aid in better tool discrimination and trajectory smoothing over time.

Implications and Future Directions

The implications of this research are multifaceted. Practically, it suggests a pathway for developing intelligent operating room systems that can autonomously interpret surgical activities, potentially enhancing intraoperative decision support and post-operative analysis. Theoretically, it paves the way for using weakly supervised learning models in high-stakes environments, where time constraints and the need for rapid deployment necessitate less demanding data curation processes.

Future developments could see the expansion of this methodology beyond laparoscopic tool tracking to other surgical domains and video-based applications. Moreover, integrating advancements in ConvLSTM networks with other cutting-edge AI techniques such as transformers, may further bolster the efficiency and accuracy of real-time tracking systems.

The insights furnished through this paper substantiate the potential for weakly supervised methodologies to transform video analysis paradigms in surgical environments, with the ConvLSTM distinguishing itself as a pivotal component in modeling temporal coherence effectively.