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

Meeting name: SPR 2025 Annual Meeting , 2025

Authors: Vasanawala Sauram, Pauly John, Gatidis Sergios

Keywords: Segmentation, Scoliosis, Deep Learning

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

Meeting name: SPR 2019 Annual Meeting & Postgraduate Course , 2019

Authors: Cole Elizabeth, Pauly John, Vasanawala Shreyas, Cheng Joseph

Keywords: Magnetic Resonance Imaging, deep learning, Neural network