Multiphase contrast-enhanced computed tomography (CT) is a popular method used by radiologists to diagnose patients with liver diseases. The analysis of multiphase images may involve image segmentation and registration. While the latter can be addressed using traditional registration techniques, it can be proven difficult when dealing with sub-optimal acquisitions. In real conditions, a registration process not only needs to accommodate patient’s motion occurring between acquisitions but also the variation in the scanner field of view that changes between the acquisitions. Actually, each image acquisition acquired during an individual exam may cover different portions of the liver, resulting in full or partial liver scans. Partial liver scans present a challenge to registration methods that expect to see the same portion of the body in the input data. Such methods would tend to register partial liver volumes to full liver volumes, and as a result, misplace locations of internal liver landmarks. In this work we present a method that registers multiphase CT scans with partial and full liver while ignoring portions of liver that are represented only in one of the scans. We assess its performance and discuss its capacity to improve tracking of liver lesions across phases.
We developed and validated a research-only deep learning (DL) based automatic algorithm to detect thoracic and abdominal aortic aneurysms on contrast and non-contrast CT images and compared its performance with assessments obtained from retrospective radiology reports. The DL algorithm was developed using 556 CT scans. Manual annotations of aorta centerlines and cross-sectional aorta boundaries were created to train the algorithm. Aorta segmentation and aneurysm detection performances were evaluated on 2263 retrospective CT scans (154 thoracic and 176 abdominal aneurysms). Evaluation was performed by comparing the automatically detected aneurysm status to the aneurysm status reported in the radiology reports and the AUC was reported. In addition, a quantitative evaluation was performed to compare the automatically measured aortic diameters to manual diameters on a subset of 59 CT scans. Pearson correlation coefficient was used. For aneurysm detection, the AUC was 0.95 for thoracic aneurysm detection (95% confidence region [0.93, 0.97]) and 0.94 for abdominal aneurysm detection (95% confidence region [0.92, 0.96]). For aortic diameter measurement, the Pearson correlation coefficient was 0.973 (p<0.001).
KEYWORDS: Liver, 3D modeling, Computed tomography, 3D image processing, Feature extraction, Liver cancer, Algorithm development, Medical imaging, Diagnostics, Cancer
There are many different sources of information beyond the actual images that serve to inform radiologists when they are making diagnoses. Therefore, it is important to include such information, referred to as context, in machine learning algorithms designed for automated classification. To better utilize and to explore the power of context in medical imaging we developed a classification algorithm based on convolutional neural networks (CNNs) with various contexts to classify liver lesions in multi-phase computed tomography data. We designed an algorithm, referred to as sliced 3D, to efficiently handle 3D context in the image data. We further included clinical context summarizing the patient’s medical status to improve performance, and we also exploited the presence or absence of other lesions in the volume to inform classification of a lesion under consideration. The effect of co-occurrence is learned from the data during the training process. The algorithm was developed, validated, and tested on 2205 multi-vendor multi-institution studies. We found that the sliced 3D algorithm performed better than equivalent 2D and 3D CNN baselines (average F1=0.629 vs F1=0.589). Using the clinical and co-occurrence contexts further improved the algorithm’s performance (average F1=0.734). Our evaluation demonstrates that this novel CNN architecture, in conjunction with additional information about medical status and lesion co-occurrence, substantially improves classification results.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.