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Surgical phase classification and operative skill assessment through spatial context aware CNNs and time-invariant feature extracting autoencoders

Pradeep Chakka Sai, Sinha Neelam

Biocybernetics and Biomedical Engineering

2023

Vol. 43, no. 4

700--724

Automated surgical video analysis promises improved healthcare. We propose novel spatial context aware combined loss function for end-to-end Encoder-Decoder training for Surgical Phase Classification (SPC) on laparoscopic cholecystectomy (LC) videos. Proposed loss function leverages on fine-grained class activation maps obtained from fused multi-layer Layer-CAM for supervised learning of SPC, obtaining improved Layer-CAM explanations. Post classification, we introduce graph theory to incorporate known hierarchies of surgical phases. We report peak SPC accuracy of 96.16%, precision of 94.08% and recall of 90.02% on public dataset Cholec80, with 7 phases. Our proposed method utilizes just 73.5% of parameters as against existing state-of-the-art methodology, achieving improvement of 0.5% in accuracy, 1.76% in precision with comparable recall, with an order less standard deviation. We also propose DNN based surgical skill assessment methodology. This approach utilizes surgical phase prediction scores from the final fully-connected layer of spatial-context aware classifier to form multi-channel temporal signal of surgical phases. Time-invariant representation is obtained from this temporal signal through time- and frequency-domain analyses. Autoencoder based time-invariant features are utilized for reconstruction and identification of prominent peaks in dissimilarity curves. We devise a surgical skill measure (SSM) based on spatial-context aware temporal-prominence-of-peaks curve. SSM values are expected to be high when executed skillfully, aligning with expert assessed GOALS metric. We illustrate this trend on Cholec80 and m2cai16-tool datasets, in comparison with GOALS metric. Concurrence in the trend of SSM with respect to GOALS metric is obtained on these test videos, making it a promising step towards automated surgical skill assessment.

ASYSTENT - control system assisting surgeon in laparoscopy

Sauer B., Kozłowski K., Waliszewski W., Michalski M., Kiełczewski M., Pazderski D., Jeziorek P.

Biocybernetics and Biomedical Engineering

2006

Vol. 26, no. 4

55-70

This paper presents a concept of the multi-Ievel control system for the Minimally Invasive Surgery (MIS). The robot assistant has been proposed to help the surgeon in the laparoscopic cholecystectomy. The ASYSTENT system is the result of the cooperation between Chair of ControI and Systems Engineering, Poznań University of Technology, and The Department of General and Laparoscopic Surgery, Hospital J. Strusia in Poznań, Poland. Presented system consists of the following elements: the main unit, the joystick controI unit, the unit of force controI, the speech recognition system and the vision system. In this project the Stäubli robot RX60, equipped with the force and torque sensor JR3, and the laparoscope, has been used. Communication is realized with the help of TCP/IP protocol.