Automated Author ProfileMurali, Aditya
University of Strasbourg
Murali, Aditya
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 4.0 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Minimally invasive image-guided surgery heavily relies on vision. Deeplearning models for surgical video analysis can support surgeons in visualtasks such as assessing the critical view of safety (CVS) in laparoscopiccholecystectomy, potentially contributing to surgical safety and efficiency.However, the performance, reliability, and reproducibility of such models aredeeply dependent on the availability of data with high-quality annotations. Tothis end, we release Endoscapes2023, a dataset comprising 201 laparoscopiccholecystectomy videos with regularly spaced frames annotated withsegmentation masks of surgical instruments and hepatocystic anatomy, as wellas assessments of the criteria defining the CVS by three trained surgeonsfollowing a public protocol. Endoscapes2023 enables the development of modelsfor object detection, semantic and instance segmentation, and CVS prediction,contributing to safe laparoscopic cholecystectomy.
Authors
- Mascagni, Pietro ;
- Alapatt, Deepak ;
- Murali, Aditya ;
- Vardazaryan, Armine ;
- Garcia Vazquez, Alain ;
- Okamoto, Nariaki ;
- Costamagna, Guido ;
- Mutter, Didier ;
- Marescaux, Jacques ;
- Dallemagne, Bernard ;
- Padoy, Nicolas
Minimally invasive image-guided surgery heavily relies on vision. Deeplearning models for surgical video analysis can support surgeons in visualtasks such as assessing the critical view of safety (CVS) in laparoscopiccholecystectomy, potentially contributing to surgical safety and efficiency.However, the performance, reliability, and reproducibility of such models aredeeply dependent on the availability of data with high-quality annotations. Tothis end, we release Endoscapes2023, a dataset comprising 201 laparoscopiccholecystectomy videos with regularly spaced frames annotated withsegmentation masks of surgical instruments and hepatocystic anatomy, as wellas assessments of the criteria defining the CVS by three trained surgeonsfollowing a public protocol. Endoscapes2023 enables the development of modelsfor object detection, semantic and instance segmentation, and CVS prediction,contributing to safe laparoscopic cholecystectomy.
Authors
- Mascagni, Pietro ;
- Alapatt, Deepak ;
- Murali, Aditya ;
- Vardazaryan, Armine ;
- Garcia Vazquez, Alain ;
- Okamoto, Nariaki ;
- Costamagna, Guido ;
- Mutter, Didier ;
- Marescaux, Jacques ;
- Dallemagne, Bernard ;
- Padoy, Nicolas