Neng Dai

Ni Yao, Yanhui Tian, Daniel Gama das Neves et al.

Epicardial adipose tissue (EAT) is known for its pro-inflammatory properties and association with Coronavirus Disease 2019 (COVID-19) severity. However, current EAT segmentation methods do not consider positional information. Additionally, the detection of COVID-19 severity lacks consideration for EAT radiomics features, which limits interpretability. This study investigates the use of radiomics features from EAT and lungs to detect the severity of COVID-19 infections. A retrospective analysis of 515 patients with COVID-19 (Cohort1: 415, Cohort2: 100) was conducted using a proposed three-stage deep learning approach for EAT extraction. Lung segmentation was achieved using a published method. A hybrid model for detecting the severity of COVID-19 was built in a derivation cohort, and its performance and uncertainty were evaluated in internal (125, Cohort1) and external (100, Cohort2) validation cohorts. For EAT extraction, the Dice similarity coefficients (DSC) of the two centers were 0.972 (+-0.011) and 0.968 (+-0.005), respectively. For severity detection, the hybrid model with radiomics features of both lungs and EAT showed improvements in AUC, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) compared to the model with only lung radiomics features. The hybrid model exhibited an increase of 0.1 (p<0.001), 19.3%, and 18.0% respectively, in the internal validation cohort and an increase of 0.09 (p<0.001), 18.0%, and 18.0%, respectively, in the external validation cohort while outperforming existing detection methods. Uncertainty quantification and radiomics features analysis confirmed the interpretability of case prediction after inclusion of EAT features.

Fubao Zhu, Zhengyuan Gao, Chen Zhao et al.

Intravascular ultrasound (IVUS) imaging allows direct visualization of the coronary vessel wall and is suitable for the assessment of atherosclerosis and the degree of stenosis. Accurate segmentation and measurements of lumen and median-adventitia (MA) from IVUS are essential for such a successful clinical evaluation. However, current segmentation relies on manual operations, which is time-consuming and user-dependent. In this paper, we aim to develop a deep learning-based method using an encoder-decoder deep architecture to automatically extract both lumen and MA border. Our method named IVUS-U-Net++ is an extension of the well-known U-Net++ model. More specifically, a feature pyramid network was added to the U-Net++ model, enabling the utilization of feature maps at different scales. As a result, the accuracy of the probability map and subsequent segmentation have been improved We collected 1746 IVUS images from 18 patients in this study. The whole dataset was split into a training dataset (1572 images) for the 10-fold cross-validation and a test dataset (174 images) for evaluating the performance of models. Our IVUS-U-Net++ segmentation model achieved a Jaccard measure (JM) of 0.9412, a Hausdorff distance (HD) of 0.0639 mm for the lumen border, and a JM of 0.9509, an HD of 0.0867 mm for the MA border, respectively. Moreover, the Pearson correlation and Bland-Altman analyses were performed to evaluate the correlations of 12 clinical parameters measured from our segmentation results and the ground truth, and automatic measurements agreed well with those from the ground truth (all Ps<0.01). In conclusion, our preliminary results demonstrate that the proposed IVUS-U-Net++ model has great promise for clinical use.