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

Vasanawala Sauram,  Pauly John,  Gatidis Sergios

Final Pr. ID: Poster #: SCI-004

Automated segmentation is an important step in automated processing of radiographs. However, manual generation of training data is tedious. To overcome this challenge, we leverage synthetic radiograph training data generated from CT volumes together with corresponding anatomy segmentation labels.
We demonstrate the effectiveness of this approach on the use case of automated scoliosis Cobb angle quantification. Read More

Authors:  Vasanawala Sauram , Pauly John , Gatidis Sergios

Keywords:  Segmentation, Scoliosis, Deep Learning

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

Mahalingam Neeraja,  Bates Alister,  Higano Nara,  Gunatilaka Chamindu,  Woods Jason,  Somasundaram Elanchezhian

Final Pr. ID: Poster #: SCI-010

To develop an optimized AI model to automatically segment lung volumes from pulmonary magnetic resonance images (MRI) and generate tidal volume calculations for neonatal patients with chronic lung disease of prematurity (bronchopulmonary dysplasia, BPD). Read More

Authors:  Mahalingam Neeraja , Bates Alister , Higano Nara , Gunatilaka Chamindu , Woods Jason , Somasundaram Elanchezhian

Keywords:  Bronchopulmonary Dysplasia, MRI, 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

Keywords:  Cardiac, MRI, Deep Learning

Randhawa Hari,  Park Brandon,  Khanna Praneet,  Jung Daniel,  Das Ayushman,  Ginn Kevin,  Mitchell Grace

Final Pr. ID: Poster #: EDU-003

MRI is crucial to the diagnosis, treatment planning, and monitoring of pediatric posterior fossa tumors; recent advances in machine and deep learning offer the prospect of increased diagnostic accuracy and minimally invasive management strategies. Machine learning and deep learning represent two artificial intelligence (AI) applications of radiomics that show promising results in the identification and classification of pediatric posterior fossa tumors, such as medulloblastoma, diffuse intrinsic pontine glioma, ependymoma, and pilocytic astrocytoma, based on MRI data. Many AI models demonstrate comparable, or in some cases, better, performance than human radiologists. Beyond diagnosis, machine and deep learning algorithms have been developed for tumor histology determination, prognostication, and molecular subtyping, to name just a few examples. In its early stages, however, the technology has several barriers to overcome before mainstream use. Studies of AI algorithms are often conducted with small sample sizes due to the relative rarity of pediatric posterior fossa tumors; many are also conducted at a single institution. Algorithms are susceptible to class imbalance as well, making it more difficult to identify tumors with lower prevalence. Multi-institution studies are important in addressing those limitations, though they have their own cumbersome requirements, such as data harmonization. Deep learning training strategies, such as federated learning, data augmentation, and transfer learning, help to address the problems introduced by working with small data sets. The purpose of this exhibit is to review the methods, results, and limitations identified in studies testing the efficacy of machine and deep learning models for the identification and classification of pediatric posterior fossa tumors and to discuss their potential for use in the clinical setting. Read More

Authors:  Randhawa Hari , Park Brandon , Khanna Praneet , Jung Daniel , Das Ayushman , Ginn Kevin , Mitchell Grace

Keywords:  Artificial Intelligence, Deep Learning, Machine Learning

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

Keywords:  Deep learning, Airway, Xray