Deep neural networks (DNNs) have been widely used in the medical imaging field. The large and high quality dataset is crucial for the performance of the deep learning models, but the medical data and ground-truth is often insufficient and very expensive in terms of time and human effort on the data collection. However, we can improve the performance of the deep learning model by augmenting the data we already have. In this work, we introduce a novel differential geometry-based quasi conformal (QC) mapping augmentation technique to augment the brain tumor images. The QC method lets the user specify or randomly generate a complex-valued function on the image domain via Beltrami coefficient. By solving the Beltrami equation with given Beltrami coefficient, the QC map, which can further guide the deformation of the image, is able to generate all possible linear and non-linear image warpings and it is flexible to allow the user to fully control the global and local deformations. Our experimental results demonstrate the efficiency and efficacy of the proposed method.
Glioblastoma multiforme (GBM) is the largest and most genetically and phenotypically heterogeneous category of primary brain tumors. Numerous novel chemical, targeted molecular and immune-active therapies in trial produce promising responses in a small disparate subset of patients but which patient will respond to which therapy remains unpredictable. Reliable imaging biomarkers for prediction and early detection of treatment response and survival are critical needs in neuro-oncology. In this study, brain tumor MRI 'deep features' extracted via transfer learning techniques were combined with features derived from an explicitly designed radiomics model to search for MRI markers predictive of overall survival (OS) in GBM patients. Two pre-trained convolutional neural network (CNN) models were utilized as the deep learning models and the elastic net-Cox model was performed to distinguish GBM patients into two survival groups. Two patient cohorts were included in this study. One was 50 GBM patients from our hospital and the other was 128 GBM patients from the Cancer Genome Atlas (TCGA) and the Cancer Image Archive (TCIA). The combined feature framework was predictive of OS in both data set with log-rank test p-value < 0.05 and may merit further study for reproducible prediction of treatment response.
Proc. SPIE. 4685, Medical Imaging 2002: PACS and Integrated Medical Information Systems: Design and Evaluation
KEYWORDS: Data modeling, Visualization, Imaging systems, Databases, Image processing, Computing systems, Data acquisition, Telecommunications, Analytical research, Picture Archiving and Communication System
The purpose of this paper is to demonstrate the importance of building a brain imaging registry (BIR) on top of existing medical information systems including Picture Archiving Communication Systems (PACS) environment. We describe the design framework for a cluster of data marts whose purpose is to provide clinicians and researchers efficient access to a large volume of raw and processed patient images and associated data originating from multiple operational systems over time and spread out across different hospital departments and laboratories. The framework is designed using object-oriented analysis and design methodology. The BIR data marts each contain complete image and textual data relating to patients with a particular disease.