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

• Autorzy: Pradeep Chakka Sai , Sinha Neelam

Identyfikatory

DOI

10.1016/j.bbe.2023.10.001

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

laparoscopic cholecystectomy; surgical workflow analysis; class activation map; graph theory; automated surgical skill assessment; time series analysis

PL

cholecystektomia laparoskopowa; teoria grafów; analiza szeregów czasowych

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2023
• Tom: Vol. 43, no. 4
• Strony: 700--724
• Opis fizyczny: Bibliogr. 69 poz., rys., tab., wykr.

Twórcy

autor

Pradeep Chakka Sai

• International Institute of Information Technology Bangalore, 26/C, Hosur Rd, Electronics City Phase 1, Electronic City, Bangalore 560100, Karnataka, India

autor

Sinha Neelam

• International Institute of Information Technology Bangalore, Electronic City, Bangalore, Karnataka, India

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).