@inproceedings{e06c6b82e98e47d8b7c3011f31a671a4,
title = "Automatic detection of wrist fractures from posteroanterior and lateral radiographs: A deep learning-based approach",
abstract = "We present a system that uses convolutional neural networks (CNNs) to detect wrist fractures (distal radius fractures) in posterioanterior and lateral radiographs. The proposed system uses random forest regression voting constrained local model to automatically segment the radius. The resulting automatic annotation is used to register the object across the dataset and crop patches. A CNN is trained on the registered patches for each view separately. Our automatic system outperformed existing systems with a performance of 96% (area under receiver operating characteristic curve) on cross-validation experiments on a dataset of 1010 patients, half of them with fractures.",
keywords = "Computer-aided diagnosis, Medical image analysis with deep learning, Wrist fracture detection, X-ray fracture detection",
author = "Raja Ebsim and Jawad Naqvi and Cootes, {Timothy F.}",
year = "2019",
doi = "10.1007/978-3-030-11166-3_10",
language = "English",
isbn = "9783030111656",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "114--125",
editor = "Guoyan Zheng and Toma{\v z} Vrtovec and Jianhua Yao and Pozo, {Jose M.}",
booktitle = "Computational Methods and Clinical Applications in Musculoskeletal Imaging - 6th International Workshop, MSKI 2018, Held in Conjunction with MICCAI 2018, Revised Selected Papers",
address = "United States",
note = "6th International Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging, MSKI 2018 was held in conjunction with Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018 ; Conference date: 16-09-2018 Through 20-09-2018",
}