Placental Flattening via Volumetric Parameterization

We present a volumetric mesh-based algorithm for flattening the placenta to a canonical template to enable effective visualization of local anatomy and function. Monitoring placental function in vivo promises to support pregnancy assessment and to improve care outcomes. We aim to alleviate visualization and interpretation challenges presented by the shape of the placenta when it is attached to the curved uterine wall. To do so, we flatten the volumetric mesh that captures placental shape to resemble the well-studied ex vivo shape. We formulate our method as a map from the in vivo shape to a flattened template that minimizes the symmetric Dirichlet energy to control distortion throughout the volume. Local injectivity is enforced via constrained line search during gradient descent. We evaluate the proposed method on 28 placenta shapes extracted from MRI images in a clinical study of placental function. We achieve sub-voxel accuracy in mapping the boundary of the placenta to the template while successfully controlling distortion throughout the volume. We illustrate how the resulting mapping of the placenta enhances visualization of placental anatomy and function. Our code is freely available at https://github.com/mabulnaga/placenta-flattening .Comment: MICCAI 201

Indiana University’s Affordable E-text Model and Strategies to Increase Impact Beyond Savings – The Evolution of Affordable Content Efforts in the Higher Education Environment: Programs, Case Studies, and Examples

Hyperinflation in overall college costs and in textbook prices in the last decade is no secret, as demonstrated by extensive coverage in several major news reports. High textbook prices are deterring students from buying required textbooks or taking courses that have high-priced textbooks. The cost of textbooks, in other words, gets in the way of learning, and derails students from degrees they want to pursue in college. Many universities and even state systems (Acker, 2011) are trying to lower the cost of textbooks for students. Indiana University (IU) started a pilot e-text program in 2009 to make publisher/commercial content more affordable. The program is based on an inclusive access model, addressed in more detail in the previous two chapters of this book. After a successful pilot phase, the IU eTexts Program moved into full production. As of March, 2018, it has served close to 200,000 students in over 9,000 course sections, and saved them more than $13 million in textbook costs. In this chapter, we explain the Indiana University eText model, the research efforts behind the program, support for faculty and students, and the factors behind the program’s success

Effects of E‐textbook Instructor Annotations on Learner Performance

With additional features and increasing cost advantages, e-textbooks are becoming a viable alternative to paper textbooks. One important feature offered by enhanced e-textbooks (e-textbooks with interactive functionality) is the ability for instructors to annotate passages with additional insights. This paper describes a pilot study that examines the effects of instructor e-textbook annotations on student learning as measured by multiple-choice and open-ended test items. Fifty-two college students in a business course were randomly assigned either a paper or an electronic version of a textbook chapter. Results show that the e-textbook group outperformed the paper textbook group on the open-ended test item, while both groups performed equally on the multiple-choice subject test. These results suggest that the instructional affordances that an interactive e-textbook provides may lead to higher-level learning

AnyStar: Domain randomized universal star-convex 3D instance segmentation

Star-convex shapes arise across bio-microscopy and radiology in the form of nuclei, nodules, metastases, and other units. Existing instance segmentation networks for such structures train on densely labeled instances for each dataset, which requires substantial and often impractical manual annotation effort. Further, significant reengineering or finetuning is needed when presented with new datasets and imaging modalities due to changes in contrast, shape, orientation, resolution, and density. We present AnyStar, a domain-randomized generative model that simulates synthetic training data of blob-like objects with randomized appearance, environments, and imaging physics to train general-purpose star-convex instance segmentation networks. As a result, networks trained using our generative model do not require annotated images from unseen datasets. A single network trained on our synthesized data accurately 3D segments C. elegans and P. dumerilii nuclei in fluorescence microscopy, mouse cortical nuclei in micro-CT, zebrafish brain nuclei in EM, and placental cotyledons in human fetal MRI, all without any retraining, finetuning, transfer learning, or domain adaptation. Code is available at https://github.com/neel-dey/AnyStar.Comment: Code available at https://github.com/neel-dey/AnySta

A comparison of electrochemical degradation of phenol on boron doped diamond and lead dioxide anodes

This work compares two electrode materials used to mineralize phenol contained in waste waters. Two disks covered with either boron doped diamond (BDD) or PbO2 were used as anodes in a one compartment flow cell under the same hydrodynamic conditions. Efficiencies of galvanostatic electrolyses are compared on the basis of measurements of Total Organic Carbon (TOC) and Chemical Oxygen Demand (COD). Galvanostatic electrolyses were monitored by analysis of phenol and of its oxidation derivatives to evaluate the operating time needed for complete elimination of toxic aromatics. The experimental current efficiency is close to the theoretical value for the BDD electrode. Other parameters being equal, phenol species disappeared at the same rate using the two electrode materials but the BDD anode showed better efficiency to eliminate TOC and COD. Moreover, during the electrolysis less intermediates are formed with BDD compared to PbO2 whatever the current density. A comparison of energy consumption is given based on the criterion of 99% removal of aromatic compounds

Dynamic Neural Fields for Learning Atlases of 4D Fetal MRI Time-series

We present a method for fast biomedical image atlas construction using neural fields. Atlases are key to biomedical image analysis tasks, yet conventional and deep network estimation methods remain time-intensive. In this preliminary work, we frame subject-specific atlas building as learning a neural field of deformable spatiotemporal observations. We apply our method to learning subject-specific atlases and motion stabilization of dynamic BOLD MRI time-series of fetuses in utero. Our method yields high-quality atlases of fetal BOLD time-series with $\sim$5-7$\times$ faster convergence compared to existing work. While our method slightly underperforms well-tuned baselines in terms of anatomical overlap, it estimates templates significantly faster, thus enabling rapid processing and stabilization of large databases of 4D dynamic MRI acquisitions. Code is available at https://github.com/Kidrauh/neural-atlasingComment: 6 pages, 2 figures. Accepted by Medical Imaging Meets NeurIPS 202

A Joint Local and Global Deep Metric Learning Method for Caricature Recognition

Caricature recognition is a novel, interesting, yet challenging problem. Due to the exaggeration and distortion, there is a large cross-modal gap between photographs and caricatures, making it nontrivial to match the features of photographs and caricatures. To address the problem, a joint local and global metric learning method (LGDML) is proposed. First, joint local and global feature representation is learnt with convolutional neural networks to find both discriminant features of local facial parts and global distinctive features of the whole face. Next, in order to fuse the local and global similarities of features, a unified feature representation and similarity measure learning framework is proposed. Various methods are evaluated on the caricature recognition task. We have verified that both local and global features are crucial for caricature recognition. Moreover, experimental results show that, compared with the state-of-the-art methods, LGDML can obtain superior performance in terms of Rank-1 and Rank-10

Impaired Collateral Recruitment and Outward Remodeling in Experimental Diabetes

OBJECTIVE—In this study, the effect of chronic hyperglycemia on acute ligation-induced collateral vasodilation, on monocyte chemotaxis, and on structural outward remodeling of collaterals was investigated