期刊
INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY
卷 14, 期 6, 页码 1059-1067出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s11548-019-01958-6
关键词
Surgical workflow analysis; Tool tracking; Weak supervision; Spatiotemporal coherence; ConvLSTM; Endoscopic videos
资金
- French state funds
- ANR [ANR-11-LABX-0004, ANR-10-IAHU-02]
PurposeReal-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.MethodsThe 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 spatiotemporal ability to smooth the class peak activations in the localization heat maps (Lh-maps).ResultsWe 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.ConclusionsIn 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 spatiotemporal coherence of consecutive image frames across a surgical video to improve tool presence detection, spatial localization, and motion tracking.
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