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.
Cardiovascular disease is the number one cause of mortality worldwide. Risk prediction can help incentivize lifestyle changes and inform targeted preventative treatment. In this work we explore utilizing a convolutional neural network (CNN) to predict cardiovascular disease risk from abdominal CT scans taken for routine CT colonography in otherwise healthy patients aged 50-65. We find that adding a variational autoencoder (VAE) to the CNN classifier improves its accuracy for five year survival prediction (AUC 0.787 vs. 0.768). In four-fold cross validation we obtain an average AUC of 0.787 for predicting five year survival and an AUC of 0.767 for predicting cardiovascular disease. For five year survival prediction our model is significantly better than the Framingham Risk Score (AUC 0.688) and of nearly equivalent performance to method demonstrated in Pickhardt et al. (AUC 0.789) which utilized a combination of five CT derived biomarkers.
The vertebral levels of the spine provide a useful coordinate system when making measurements of plaque, muscle, fat, and bone mineral density. Correctly classifying vertebral levels with high accuracy is challenging due to the similar appearance of each vertebra, the curvature of the spine, and the possibility of anomalies such as fractured vertebrae, implants, lumbarization of the sacrum, and sacralization of L5. The goal of this work is to develop a system that can accurately and robustly identify the L1 level in large heterogeneous datasets. The first approach we study is using a 3D U-Net to segment the L1 vertebra directly using the entire scan volume to provide context. We also tested models for two class segmentation of L1 and T12 and a three class segmentation of L1, T12 and the rib attached to T12. By increasing the number of training examples to 249 scans using pseudo-segmentations from an in-house segmentation tool we were able to achieve 98% accuracy with respect to identifying the L1 vertebra, with an average error of 4.5 mm in the craniocaudal level. We next developed an algorithm which performs iterative instance segmentation and classification of the entire spine with a 3D U-Net. We found the instance based approach was able to yield better segmentations of nearly the entire spine, but had lower classification accuracy for L1.
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