Landmark-Free Statistical Shape Modeling Via Neural Flow Deformations

Statistical shape modeling aims at capturing shape variations of an anatomical structure that occur within a given population. Shape models are employed in many tasks, such as shape reconstruction and image segmentation, but also shape generation and classification. Existing shape priors either require dense correspondence between training examples or lack robustness and topological guarantees. We present FlowSSM, a novel shape modeling approach that learns shape variability without requiring dense correspondence between training instances. It relies on a hierarchy of continuous deformation flows, which are parametrized by a neural network. Our model outperforms state-of-the-art methods in providing an expressive and robust shape prior for distal femur and liver. We show that the emerging latent representation is discriminative by separating healthy from pathological shapes. Ultimately, we demonstrate its effectiveness on two shape reconstruction tasks from partial data. Our source code is publicly available (https://github.com/davecasp/flowssm)

Detection of pneumonia associated pathogens using a prototype multiplexed pneumonia test in hospitalized patients with severe pneumonia

Severe pneumonia remains an important cause of morbidity and mortality. Polymerase chain reaction (PCR) has been shown to be more sensitive than current standard microbiological methods--particularly in patients with prior antibiotic treatment--and therefore, may improve the accuracy of microbiological diagnosis for hospitalized patients with pneumonia. Conventional detection techniques and multiplex PCR for 14 typical bacterial pneumonia-associated pathogens were performed on respiratory samples collected from adult hospitalized patients enrolled in a prospective multi-center study. Patients were enrolled from March until September 2012. A total of 739 fresh, native samples were eligible for analysis, of which 75 were sputa, 421 aspirates, and 234 bronchial lavages. 276 pathogens were detected by microbiology for which a valid PCR result was generated (positive or negative detection result by Curetis prototype system). Among these, 120 were identified by the prototype assay, 50 pathogens were not detected. Overall performance of the prototype for pathogen identification was 70.6% sensitivity (95% confidence interval (CI) lower bound: 63.3%, upper bound: 76.9%) and 95.2% specificity (95% CI lower bound: 94.6%, upper bound: 95.7%). Based on the study results, device cut-off settings were adjusted for future series production. The overall performance with the settings of the CE series production devices was 78.7% sensitivity (95% CI lower bound: 72.1%) and 96.6% specificity (95% CI lower bound: 96.1%). Time to result was 5.2 hours (median) for the prototype test and 43.5 h for standard-of-care. The Pneumonia Application provides a rapid and moderately sensitive assay for the detection of pneumonia-causing pathogens with minimal hands-on time