Wu Yujie, Namdar Khashayar, Chen Chaojun, Hosseinpour Shahob, Shroff Manohar, Doria Andrea, Khalvati Farzad
Final Pr. ID: Poster #: SCI-020
X-Ray based manual Cobb angle measurement is inherently time-consuming and associated with high inter- and intra-observer variability. The existing automated scoliosis measurement methods suffer from insufficient accuracy. We propose a two-step segmentation-based deep architecture to automate Cobb angle measurement for scoliosis assessment. Read More
Authors: Wu Yujie , Namdar Khashayar , Chen Chaojun , Hosseinpour Shahob , Shroff Manohar , Doria Andrea , Khalvati Farzad
Keywords: Scoliosis, Deep Learning, Cobb Angle
Castiglione James, Gilligan Leah, Somasundaram Elanchezhian, Trout Andrew, Brady Samuel
Final Pr. ID: Paper #: 059
In this study, we trained two convolutional neural networks to automatically identify the third vertebral level and segment the abdominal muscle in contrast enhanced abdominal CT images. In the future, these models will be used to determine reference ranges for skeletal muscle mass in children by age for the purpose of identifying patient characteristics associated with differences in skeletal muscle mass. Read More
Authors: Castiglione James , Gilligan Leah , Somasundaram Elanchezhian , Trout Andrew , Brady Samuel
Keywords: Neural Network, Segmentation, Deep Learning
Chen Chaojun, Namdar Khashayar, Wu Yujie, Hosseinpour Shahob, Shroff Manohar, Doria Andrea, Khalvati Farzad
Final Pr. ID: Poster #: SCI-022
Scoliosis is a deformity of the spine that affects 2-3% of the population. Currently, the reference standard for assessing scoliosis is the manual assignment of Cobb angles at the site of the curvature center using X-ray images. This process is time consuming and unreliable as it is affected by inter- and intra-observer variability. To overcome these inaccuracies, machine learning (ML) methods can be used to automate the Cobb angle measurement process. Read More
Authors: Chen Chaojun , Namdar Khashayar , Wu Yujie , Hosseinpour Shahob , Shroff Manohar , Doria Andrea , Khalvati Farzad
Keywords: Scoliosis, Cobb Angle, Deep Learning
Cole Elizabeth, Pauly John, Vasanawala Shreyas, Cheng Joseph
Final Pr. ID: Poster #: SCI-059
Convolutional neural networks (CNNs) have proven to be valuable in the fields of image processing and computer vision. Our work applies complex-valued CNNs to magnetic resonance imaging (MRI) to reduce scan times. The reduction of scan times has widespread pediatric benefits. A typical scan requires that patients remain still for up to an hour to produce a clear image, which is difficult for children without inducing anesthesia, which carries risks. A need exists for greatly improved MRI scan times without the loss of diagnostic accuracy. This scan time can be reduced by subsampling in k-space. We use CNNs to reconstruct images from these undersampled acquisitions. Our work investigates complex-valued CNNs for image reconstruction in lieu of two-channel real-valued CNNs. Read More
Authors: Cole Elizabeth , Pauly John , Vasanawala Shreyas , Cheng Joseph
Keywords: Magnetic Resonance Imaging, deep learning, Neural network
Zucker Evan, Sandino Christopher, Kino Aya, Lai Peng, Vasanawala Shreyas
Final Pr. ID: Paper #: 014
To assess the image quality and clinical performance of a novel accelerated, free-breathing 2D cine cardiac MR sequence with DCNN reconstruction in comparison to conventional breath-held 2D cine balanced steady-state free precession (bSSFP). Read More
Authors: Zucker Evan , Sandino Christopher , Kino Aya , Lai Peng , Vasanawala Shreyas
Mutasa Simukayi, Liu Michael, Duong Phuong, Jambawalikar Sachin, Mostoufi-moab Sogol, Jaramillo Diego
Final Pr. ID: Paper #: 112
There is growing research in diffusion tensor imaging (DTI) for providing metrics and images of physeal structure and function, particularly in evaluating children with possible growth disorders. There is a need for a faster automated process to segment the diffusion data, however. We utilized a deep learning algorithm to automatically generate a region of interest (ROI) for use in fully automatic diffusion tensor tractography of cartilage columns in the distal femoral physis. Read More
Authors: Mutasa Simukayi , Liu Michael , Duong Phuong , Jambawalikar Sachin , Mostoufi-moab Sogol , Jaramillo Diego
Keywords: Deep learning, growth disorders, MRi
Somasundaram Elanchezhian, Brady Samuel, Crotty Eric, Trout Andrew, Anton Christopher, Towbin Alexander, Coley Brian, Dillman Jonathan
Final Pr. ID: Paper #: 032
At our institution, airway radiographs are routinely checked by the radiologist to ensure diagnostic image quality prior to the technologist completing the examination. These checks interrupt the workflow for both the technologist and radiologist. In this study, we develop and validate a deep learning algorithm to detect non-diagnostic lateral airway radiographs. Read More
Authors: Somasundaram Elanchezhian , Brady Samuel , Crotty Eric , Trout Andrew , Anton Christopher , Towbin Alexander , Coley Brian , Dillman Jonathan