Search Results for author: Joseph E. Burns

Found 5 papers, 0 papers with code

Improving Vertebra Segmentation through Joint Vertebra-Rib Atlases

no code implementations1 Feb 2016 Yinong Wang, Jianhua Yao, Holger R. Roth, Joseph E. Burns, Ronald M. Summers

The use of joint vertebra-rib atlases produced a statistically significant increase in the Dice coefficient from 92. 5 $\pm$ 3. 1% to 93. 8 $\pm$ 2. 1% for the left and right transverse processes and a decrease in the mean and max surface distance from 0. 75 $\pm$ 0. 60mm and 8. 63 $\pm$ 4. 44mm to 0. 30 $\pm$ 0. 27mm and 3. 65 $\pm$ 2. 87mm, respectively.

Computed Tomography (CT) Segmentation

Deep convolutional networks for automated detection of posterior-element fractures on spine CT

no code implementations29 Jan 2016 Holger R. Roth, Yinong Wang, Jianhua Yao, Le Lu, Joseph E. Burns, Ronald M. Summers

In this work, we apply deep convolutional networks (ConvNets) for the automated detection of posterior element fractures of the spine.

Osteoporotic and Neoplastic Compression Fracture Classification on Longitudinal CT

no code implementations27 Jan 2016 Yinong Wang, Jianhua Yao, Joseph E. Burns, Ronald M. Summers

Classification of vertebral compression fractures (VCF) having osteoporotic or neoplastic origin is fundamental to the planning of treatment.

Classification General Classification

Multi-Atlas Segmentation with Joint Label Fusion of Osteoporotic Vertebral Compression Fractures on CT

no code implementations13 Jan 2016 Yinong Wang, Jianhua Yao, Holger R. Roth, Joseph E. Burns, Ronald M. Summers

The precise and accurate segmentation of the vertebral column is essential in the diagnosis and treatment of various orthopedic, neurological, and oncological traumas and pathologies.

Segmentation

Detection of Sclerotic Spine Metastases via Random Aggregation of Deep Convolutional Neural Network Classifications

no code implementations22 Jul 2014 Holger R. Roth, Jianhua Yao, Le Lu, James Stieger, Joseph E. Burns, Ronald M. Summers

In testing, the CNN is employed to assign individual probabilities for a new set of N random views that are averaged at each ROI to compute a final per-candidate classification probability.

Computed Tomography (CT)

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