KEYWORDS: Magnetic resonance imaging, Breast, 3D scanning, Scanners, 3D image processing, Convolutional neural networks, 3D modeling, Computed tomography, Machine learning, Medical imaging
Categorization of radiological images according to characteristics such as modality, scanner parameters, body part etc, is important for quality control, clinical efficiency and research. The metadata associated with images stored in the DICOM format reliably captures scanner settings such as tube current in CT or echo time (TE) in MRI. Other parameters such as image orientation, body part examined and presence of intravenous contrast, however, are not inherent to the scanner settings, and therefore require user input which is prone to human error. There is a general need for automated approaches that will appropriately categorize images, even with parameters that are not inherent to the scanner settings. These approaches should be able to process both planar 2D images and full 3D scans. In this work, we present a deep learning based approach for automatically detecting one such parameter: the presence or absence of intravenous contrast in 3D MRI scans. Contrast is manually injected by radiology staff during the imaging examination, and its presence cannot be automatically recorded in the DICOM header by the scanner. Our classifier is a convolutional neural network (CNN) based on the ResNet architecture. Our data consisted of 1000 breast MRI scans (500 scans with and 500 scans without intravenous contrast), used for training and testing a CNN on 80%/20% split, respectively. The labels for the scans were obtained from the series descriptions created by certified radiological technologists. Preliminary results of our classifier are very promising with an area under the ROC curve (AUC) of 0.98, sensitivity and specificity of 1.0 and 0.9 respectively (at the optimal ROC cut-off point), demonstrating potential usefulness in both clinical as well as research settings.
Traumatic brain injury (TBI) is a major cause of death and disability in the United States. Time to treatment is often related to patient outcome. Access to cerebral imaging data in a timely manner is a vital component of patient care. Current methods of detecting and quantifying intracranial pathology can be time-consuming and require careful review of 2D/3D patient images by a radiologist. Additional time is needed for image protocoling, acquisition, and processing. These steps often occur in series, adding more time to the process and potentially delaying time-dependent management decisions for patients with traumatic brain injury.
Our team adapted machine learning and computer vision methods to develop a technique that rapidly and automatically detects CT-identifiable lesions. Specifically, we use scale invariant feature transform (SIFT)1 and deep convolutional neural networks (CNN)2 to identify important image features that can distinguish TBI lesions from background data. Our learning algorithm is a linear support vector machine (SVM)3. Further, we also employ tools from topological data analysis (TDA) for gleaning insights into the correlation patterns between healthy and pathological data. The technique was validated using 409 CT scans of the brain, acquired via the Progesterone for the Treatment of Traumatic Brain Injury phase III clinical trial (ProTECT_III) which studied patients with moderate to severe TBI4. CT data were annotated by a central radiologist and included patients with positive and negative scans. Additionally, the largest lesion on each positive scan was manually segmented. We reserved 80% of the data for training the SVM and used the remaining 20% for testing. Preliminary results are promising with 92.55% prediction accuracy (sensitivity = 91.15%, specificity = 93.45%), indicating the potential usefulness of this technique in clinical scenarios.
A major challenge for image guided tumor ablation is the high treatment variability due to heterogeneous tissue characteristics and thermal sinks. In this work, we present a methodology to analyze the geometry of the treatment zones and treatment zone variability. Our first contribution is an applicator centric co-ordinate system which enables us to compare treatment zones and vendor specifications across patients. Our second contribution is the analysis of the shape of the ablation zone using applicator centric longitudinal 2D cross sections. We present initial results of applying this methodology to analyze the geometry and variability in synthetic examples like ellipsoid, sphere and real microwave ablation zones in lung and liver.
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