Automated Upper Tract Urothelial Carcinoma Tumor Segmentation During Ureteroscopy Using Computer Vision Techniques.
J Endourol
; 38(8): 836-842, 2024 Aug.
Article
en En
| MEDLINE
| ID: mdl-38661528
ABSTRACT
Introduction:
Endoscopic tumor ablation of upper tract urothelial carcinoma (UTUC) allows for tumor control with the benefit of renal preservation but is impacted by intraoperative visibility. We sought to develop a computer vision model for real-time, automated segmentation of UTUC tumors to augment visualization during treatment. Materials andMethods:
We collected 20 videos of endoscopic treatment of UTUC from two institutions. Frames from each video (N = 3387) were extracted and manually annotated to identify tumors and areas of ablated tumor. Three established computer vision models (U-Net, U-Net++, and UNext) were trained using these annotated frames and compared. Eighty percent of the data was used to train the models while 10% was used for both validation and testing. We evaluated the highest performing model for tumor and ablated tissue segmentation using a pixel-based analysis. The model and a video overlay depicting tumor segmentation were further evaluated intraoperatively.Results:
All 20 videos (mean 36 ± 58 seconds) demonstrated tumor identification and 12 depicted areas of ablated tumor. The U-Net model demonstrated the best performance for segmentation of both tumors (area under the receiver operating curve [AUC-ROC] of 0.96) and areas of ablated tumor (AUC-ROC of 0.90). In addition, we implemented a working system to process real-time video feeds and overlay model predictions intraoperatively. The model was able to annotate new videos at 15 frames per second.Conclusions:
Computer vision models demonstrate excellent real-time performance for automated upper tract urothelial tumor segmentation during ureteroscopy.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Ureteroscopía
Límite:
Humans
Idioma:
En
Revista:
J Endourol
Asunto de la revista:
UROLOGIA
Año:
2024
Tipo del documento:
Article
País de afiliación:
Estados Unidos