Prospective assessment of an atlas-based intervention combined with real-time software feedback in contouring lymph node levels and organs-at-risk in the head and neck: Quantitative assessment of conformance to expert delineation
Musaddiq Awan, Clifton D. Fuller(The University of Texas MD Anderson Cancer Center), Emma B. Holliday(The University of Texas MD Anderson Cancer Center), G. Brandon Gunn(The University of Texas MD Anderson Cancer Center), David I. Rosenthal(The University of Texas MD Anderson Cancer Center), Jayashree Kalpathy–Cramer(University of Colorado Denver), Abhilasha Patel(The University of Texas at San Antonio Health Science Center), Adam S. Garden(The University of Texas MD Anderson Cancer Center), Beth M. Beadle(Stanford University), Elizabeth V. Maani(The University of Texas at San Antonio Health Science Center), Bundhit Tantiwongkosi(The University of Texas at San Antonio Health Science Center), William E. Jones(The University of Texas Health Science Center at San Antonio), V. Clyburn(The University of Texas at San Antonio Health Science Center), Jehee Isabelle Choi(The University of Texas at San Antonio Health Science Center), Jack Phan(The University of Texas MD Anderson Cancer Center)
Cited by 21
Related Papers
3D Slicer as an image computing platform for the Quantitative Imaging Network
|Magnetic Resonance Imaging|2012|9.1k
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
|IEEE Transactions on Medical Imaging|2014|6.7k
Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials
|Neuro-Oncology|2015|670
Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks
|JAMA Ophthalmology|2018|660
The RSNA Pediatric Bone Age Machine Learning Challenge
|Radiology|2018|464