Automatic segmentation of thrombosed aortic dissection in post-operative CT-angiography images.

PURPOSE: The thrombus in the false lumen (FL) of aortic dissection (AD) patients is a meaningful indicator to determine aortic remodeling but difficult to measure in clinic. In this study, a novel segmentation strategy based on deep learning was proposed to automatically extract the thrombus in the FL in post-operative computed tomography angiography (CTA) images of AD patients, which provided an efficient and convenient segmentation method with high accuracy. METHODS: A two-step segmentation strategy was proposed. Each step contained a convolutional neural network (CNN) to segment the aorta and the thrombus, respectively. In the first step, a CNN was used to obtain the binary segmentation mask of the whole aorta. In the second step, another CNN was introduced to segment the thrombus. The results of the first step were used as additional input to the second step to highlight the aorta in the complex background. Moreover, skip connection attention refinement (SAR) modules were designed and added in the second step to improve the segmentation accuracy of the thrombus details by efficiently using the low-level features. RESULTS: The proposed method provided accurate thrombus segmentation results (0.903 ± 0.062 in dice score, 0.828 ± 0.092 in Jaccard index, and 2.209 ± 2.945 in 95% Hausdorff distance), which showed improvement compared to the methods without prior information (0.846 ± 0.085 in dice score) and the method without SAR (0.899 ± 0.060 in dice score). Moreover, the proposed method achieved 0.967 ± 0.029 and 0.948 ± 0.041 in dice score of true lumen (TL) and patent FL (PFL) segmentation, respectively, indicating the excellence of the proposed method in the segmentation task of the overall aorta. CONCLUSIONS: A novel CNN-based segmentation framework was proposed to automatically obtain thrombus segmentation for thrombosed AD in post-operative CTA images, which provided a useful tool for further application of thrombus-related indicators in clinical and research application.

Volumetric Measurements Improve the Accuracy of Aortic Remodeling Prediction in Aortic Dissection.

Accessing aortic remodeling status through regular follow-ups is essential for acute type A aortic dissection patients undergone surgical treatment. Aortic remodeling status was usually determined using diameter or area measurements of the true and false lumen in specific anatomical slices of medical images. However, these indicators only represent partial information about the aorta and can hardly characterize the overall aorta situation. In this study, we included two types of morphology features collected from computed tomography angiography images to predict the aortic remodeling. One type is the volumetric measurements of the true and false lumen, which provide a better overall description of the aorta, and the other type is the volumetric measurements of the thrombus in false lumen and the patent false lumen, which present more detailed information of the dissection. Through progressively incorporating these measurements into the construction of the remodeling prediction model, we investigated the importance of the features that describe the overall situation and that characterize aortic internal details in remodeling prediction, especially the effect of quantitative thrombosis features. The results showed that with the inclusion of the two types of volume features, the prediction accuracy of the model increased, which proves that volumetric measurements of aortic dissection, especially the volume of thrombus, are of significant value in aortic remodeling prediction, and should be paid more attention on in clinical practice and research areas. Clinical Relevance-Demonstrating the importance of volumetric measurements of true and false lumen thrombus in false lumen and patent false lumen in the prediction of aortic remodeling.

A community effort to identify and correct mislabeled samples in proteogenomic studies.

Sample mislabeling or misannotation has been a long-standing problem in scientific research, particularly prevalent in large-scale, multi-omic studies due to the complexity of multi-omic workflows. There exists an urgent need for implementing quality controls to automatically screen for and correct sample mislabels or misannotations in multi-omic studies. Here, we describe a crowdsourced precisionFDA NCI-CPTAC Multi-omics Enabled Sample Mislabeling Correction Challenge, which provides a framework for systematic benchmarking and evaluation of mislabel identification and correction methods for integrative proteogenomic studies. The challenge received a large number of submissions from domestic and international data scientists, with highly variable performance observed across the submitted methods. Post-challenge collaboration between the top-performing teams and the challenge organizers has created an open-source software, COSMO, with demonstrated high accuracy and robustness in mislabeling identification and correction in simulated and real multi-omic datasets.

On the rate-distortion performance and computational efficiency of the Karhunen-Loève transform for lossy data compression.

We examine the rate-distortion performance and computational complexity of linear transforms for lossy data compression. The goal is to better understand the performance/complexity tradeoffs associated with using the Karhunen-Loeve transform (KLT) and its fast approximations. Since the optimal transform for transform coding is unknown in general, we investigate the performance penalties associated with using the KLT by examining cases where the KLT fails, developing a new transform that corrects the KLT's failures in those examples, and then empirically testing the performance difference between this new transform and the KLT. Experiments demonstrate that while the worst KLT can yield transform coding performance at least 3 dB worse than that of alternative block transforms, the performance penalty associated with using the KLT on real data sets seems to be significantly smaller, giving at most 0.5 dB difference in our experiments. The KLT and its fast variations studied here range in complexity requirements from O(n(2)) to O(n log n) in coding vectors of dimension n. We empirically investigate the rate-distortion performance tradeoffs associated with traversing this range of options. For example, an algorithm with complexity O(n(3/2)) and memory O(n) gives 0.4 dB performance loss relative to the full KLT in our image compression experiments.