Surgical workflow recognition with 3DCNN for Sleeve Gastrectomy
| Autor: | Henry Choi, Bokai Zhang, Alexander Simes, Andrew Yoo, Amer Ghanem |
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| Rok vydání: | 2021 |
| Předmět: |
Sleeve gastrectomy
Jaccard index Computer-assisted surgery Computer science medicine.medical_treatment Biomedical Engineering ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Health Informatics Machine learning computer.software_genre Convolutional neural network Workflow Gastrectomy medicine Humans Radiology Nuclear Medicine and imaging 3D ConvNet business.industry Focal loss General Medicine Filter (signal processing) Computer Graphics and Computer-Aided Design Computer Science Applications Recurrent neural network ComputingMethodologies_PATTERNRECOGNITION Surgical workflow recognition Surgery Computer-Assisted Surgery Original Article Computer Vision and Pattern Recognition Artificial intelligence Neural Networks Computer Focus (optics) business computer |
| Zdroj: | International Journal of Computer Assisted Radiology and Surgery |
| ISSN: | 1861-6429 |
| Popis: | Purpose Surgical workflow recognition is a crucial and challenging problem when building a computer-assisted surgery system. Current techniques focus on utilizing a convolutional neural network and a recurrent neural network (CNN–RNN) to solve the surgical workflow recognition problem. In this paper, we attempt to use a deep 3DCNN to solve this problem. Methods In order to tackle the surgical workflow recognition problem and the imbalanced data problem, we implement a 3DCNN workflow referred to as I3D-FL-PKF. We utilize focal loss (FL) to train a 3DCNN architecture known as Inflated 3D ConvNet (I3D) for surgical workflow recognition. We use prior knowledge filtering (PKF) to filter the recognition results. Results We evaluate our proposed workflow on a large sleeve gastrectomy surgical video dataset. We show that focal loss can help to address the imbalanced data problem. We show that our PKF can be used to generate smoothed prediction results and improve the overall accuracy. We show that the proposed workflow achieves 84.16% frame-level accuracy and reaches a weighted Jaccard score of 0.7327 which outperforms traditional CNN–RNN design. Conclusion The proposed workflow can obtain consistent and smooth predictions not only within the surgical phases but also for phase transitions. By utilizing focal loss and prior knowledge filtering, our implementation of deep 3DCNN has great potential to solve surgical workflow recognition problems for clinical practice. |
| Databáze: | OpenAIRE |
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