Reticular pseudodrusen (RPD) are subretinal drusenoid deposits that represent an important disease feature in age-related macular degeneration (AMD). RPD are of particular interest because their presence is a strong predictor of progression to advanced AMD. RPD features can be characterized using volumetric spectral-domain optical coherence tomography (SD-OCT). In this work, we curated a dataset from the Age-Related Eye Diseases Study 2 (AREDS2) ancillary OCT study. The dataset included 826 SD-OCT scans, with RPD present in 222 SD-OCT scans. Binary RPD labels were transferred from fundus autofluorescence (FAF) images taken at the same visits as the SD-OCT scans. The dataset was split at the participant level into training (70%), validation (10%), and test sets (20%). We proposed a 3D classification network to detect RPD from SD-OCT scans. We compared it to a baseline 2D network with average bagging and a 3D network with multi-tasking. The proposed network achieved the highest accuracy of 0.7784, area under receiver characteristic operating curve of 0.8689, and mean average precision of 0.7706 for detecting RPD from SD-OCT scans.
Robust localization of lymph nodes (LNs) in multiparametric MRI (mpMRI) is critical for the assessment of lymphadenopathy. Radiologists routinely measure the size of LN to distinguish benign from malignant nodes, which would require subsequent cancer staging. Sizing is a cumbersome task compounded by the diverse appearances of LNs in mpMRI, which renders their measurement difficult. Furthermore, smaller and potentially metastatic LNs could be missed during a busy clinical day. To alleviate these imaging and workflow problems, we propose a pipeline to universally detect both benign and metastatic nodes in the body for their ensuing measurement. The recently proposed VFNet neural network was employed to identify LN in T2 fat suppressed and diffusion weighted imaging (DWI) sequences acquired by various scanners with a variety of exam protocols. We also use a selective augmentation technique known as Intra-Label LISA (ILL) to diversify the input data samples the model sees during training, such that it improves its robustness during the evaluation phase. We achieved a sensitivity of ∼83% with ILL vs. ∼80% without ILL at 4 FP/vol. Compared with current LN detection approaches evaluated on mpMRI, we show a sensitivity improvement of ∼9% at 4 FP/vol.
Geographic atrophy (GA) is the defining lesion of advanced atrophic age-related macular degeneration (AMD). GA can be detected and characterized most accurately using spectral-domain optical coherence tomography (SDOCT), which provides detailed 3D information about changes in multiple retinal layers. Existing methods are limited to 2D convolutional neural networks (CNNs). Therefore, they do not capture the 3D context between adjacent 2D slices of the OCT scan and also require a large inference time. We propose 3D CNNs with 3D attention mechanisms for the automated detection of GA on SDOCT scans using scan-level labels. The best network achieved an accuracy of 88%, and its visualizations suggest the interpretability of its predictions.
Identification of lymph nodes (LN) in T2 Magnetic Resonance Imaging (MRI) is an important step performed by radiologists during the assessment of lymphoproliferative diseases. The size of the nodes play a crucial role in their staging, and radiologists sometimes use an additional contrast sequence such as diffusion weighted imaging (DWI) for confirmation. However, lymph nodes have diverse appearances in T2 MRI scans, making it tough to stage for metastasis. Furthermore, radiologists often miss smaller metastatic lymph nodes over the course of a busy day. To deal with these issues, we propose to use the DEtection TRansformer (DETR) network to localize suspicious metastatic lymph nodes for staging in challenging T2 MRI scans acquired by different scanners and exam protocols. False positives (FP) were reduced through a bounding box fusion technique, and a precision of 65.41% and sensitivity of 91.66% at 4 FP per image was achieved. To the best of our knowledge, our results improve upon the current state-of-the-art for lymph node detection in T2 MRI scans.
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.