NephCNN: A deep-learning framework for vessel segmentation in nephrectomy laparoscopic videos
| Autor: | Sara Moccia, Alessandro Casella, Leonardo S. Mattos, Chiara Carlini, Elena De Momi, Emanuele Frontoni |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
business.industry
Computer science medicine.medical_treatment Deep learning Pixel connectivity kidney segmentation medicine.disease Convolutional neural network Nephrectomy fully convolutional neural networks Renal cell carcinoma Encoding (memory) adversarial training robotic surgery medicine blood vessel segmentation nephrectomy Robotic surgery Segmentation Computer vision Artificial intelligence business kidney segmentation fully convolutional neural networks adversarial training blood vessel segmentation robotic surgery nephrectomy |
| Zdroj: | ICPR |
| Popis: | Objective: In the last years, Robot-assisted partial nephrectomy (RAPN) is establishing as elected treatment for renal cell carcinoma (RCC). Reduced field of view, field occlusions by surgical tools, and reduced maneuverability may potentially cause accidents, such as unwanted vessel resection with consequent bleeding. Surgical Data Science (SDS) can provide effective context-aware tools for supporting surgeons. However, currently no tools have been exploited for automatic vessels segmentation from nephrectomy laparoscopic videos. Herein, we propose a new approach based on adversarial Fully Convolutional Neural Networks (FCNNs) to kidney vessel segmentation from nephrectomy laparoscopic vision. Methods: The proposed approach enhances existing segmentation framework by (i) encoding 3D kernels for spatio-temporal features extraction to enforce pixel connectivity in time, and (ii) perform training in adversarial fashion, which constrains vessels shape. Results: We performed a preliminary study using 8 different RAPN videos (1871 frames), the first in the field, achieving a median Dice Similarity Coefficient of 71.76%. Conclusions: Results showed that the proposed approach could be a valuable solution with a view to assist surgeon during RAPN. |
| Databáze: | OpenAIRE |
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