Both qualitative and quantitative evaluations have shown that the presented algorithm is able to accurately segment the aorta in low-dose non-contrast CT images.

No abstract

A three-dimensional (3-D) convolutional neural network (CNN) trained from scratch is presented for the classification of pulmonary nodule malignancy from low-dose chest CT scans. Recent approval of lung cancer screening in the United States provides motivation for determining the likelihood of malignancy of pulmonary nodules from the initial CT scan finding to minimize the number of follow-up actions. Classifier ensembles of different combinations of the 3-D CNN and traditional machine learning models based on handcrafted 3-D image features are also explored. The dataset consisting of 326 nodules is constructed with balanced size and class distribution with the malignancy status pathologically confirmed. The results show that both the 3-D CNN single model and the ensemble models with 3-D CNN outperform the respective counterparts constructed using only traditional models. Moreover, complementary information can be learned by the 3-D CNN and the conventional models, which together are combined to construct an ensemble model with statistically superior performance compared with the single traditional model. The performance of the 3-D CNN model demonstrates the potential for improving the lung cancer screening follow-up protocol, which currently mainly depends on the nodule size.

Nodule classification in the context of low-resolution low-dose whole-chest CT images for the clinically relevant size range in the context of lung cancer screening is highly challenging, and results are moderate compared to what has been reported in the literature for other clinical contexts. Nodule class size distribution imbalance needs to be considered in the training and evaluation of computer-aided diagnostic systems for producing patient-relevant outcomes.

Inspired by complex multifunctional leaves, in this study, we created robust hierarchically wrinkled nanoporous polytetrafluoroethene (PTFE) surfaces that exhibit superhydrophobic properties by combination of PTFE micellization and spontaneous surface wrinkling on a commercially available thermoretractable polystyrene (PS) sheet. A PTFE dispersion was coated onto the PS sheet, followed by thermal treatment to remove the surfactants surrounding the PTFE particles, and surface wrinkling was induced through a dynamic thermal contraction process. Thermally induced contraction from the PS sheet provided the driving force for developing and stabilizing micrometer-sized wrinkle formation, whereas the nanometer-sized PTFE particle aggregation formed a rigid nanoporous film, providing its intrinsic hydrophobic character. By combining the hierarchical interfacial structure and chemical composition, hierarchically wrinkled nanoporous PTFE surfaces were fabricated, which exhibited extremely high water repellence (water contact angle of ∼167°) and a water rolling-off angle lower than 5°. The wrinkled patterns could intimately bind the nanoporous PTFE layer through enhanced adhesion from their curved surface and viscous liquid surfactants, making these surfaces mechanically robust and offering potentially extendable alternatives with self-cleaning, antifouling, and drag-reducing properties.