Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Autosegmentation for thoracic radiation treatment planning: A grand challenge at AAPM 2017
244
Zitationen
17
Autoren
2018
Jahr
Abstract
The results of the challenge showed that the lungs and heart can be segmented fairly accurately by various algorithms, while deep-learning methods performed better on the esophagus. Our dataset together with the manual contours for all training cases continues to be available publicly as an ongoing benchmarking resource.
Ähnliche Arbeiten
Radiative Transfer
1950 · 8.660 Zit.
Practical cone-beam algorithm
1984 · 6.206 Zit.
Toxicity criteria of the Radiation Therapy Oncology Group (RTOG) and the European organization for research and treatment of cancer (EORTC)
1995 · 4.808 Zit.
Tolerance of normal tissue to therapeutic irradiation
1991 · 4.458 Zit.
Clonogenic assay of cells in vitro
2006 · 4.122 Zit.
Autoren
Institutionen
- The University of Texas MD Anderson Cancer Center(US)
- Memorial Sloan Kettering Cancer Center(US)
- University of Chicago(US)
- National Cancer Institute(US)
- Frederick National Laboratory for Cancer Research(US)
- Massachusetts General Hospital(US)
- Harvard University(US)
- Maastricht University(NL)
- Maastro Clinic(NL)
- Elekta (United States)(US)
- University of Virginia(US)
- Mirada Medical (United Kingdom)(GB)
- University of Minho(PT)
- Beaumont Health(US)
- Washington University in St. Louis(US)