MOF Template-Derived Carbon Shell-Embedded CoP Hierarchical Nanosheet as Bifunctional Catalyst for Overall Water Splitting.

The design of earth-abundant and highly efficient bifunctional electrocatalysts for hydrogen evolution and oxygen evolution reactions (HER/OER) is crucial for hydrogen production through overall water splitting. Herein, we report a novel nanostructure consisting of vertically oriented CoP hierarchical nanosheet arrays with in situ-assembled carbon skeletons on a Ti foil electrode. The novel Zeolitic Imidazolate Framework-67 (ZIF-67) template-derived hierarchical nanosheet architecture effectively improved electrical conductivity, facilitated electrolyte transport, and increased the exposure of the active sites.

A Data Colocation Grid Framework for Big Data Medical Image Processing: Backend Design.

When processing large medical imaging studies, adopting high performance grid computing resources rapidly becomes important. We recently presented a "medical image processing-as-a-service" grid framework that offers promise in utilizing the Apache Hadoop ecosystem and HBase for data colocation by moving computation close to medical image storage. However, the framework has not yet proven to be easy to use in a heterogeneous hardware environment.

Splenomegaly Segmentation using Global Convolutional Kernels and Conditional Generative Adversarial Networks.

Spleen volume estimation using automated image segmentation technique may be used to detect splenomegaly (abnormally enlarged spleen) on Magnetic Resonance Imaging (MRI) scans. In recent years, Deep Convolutional Neural Networks (DCNN) segmentation methods have demonstrated advantages for abdominal organ segmentation. However, variations in both size and shape of the spleen on MRI images may result in large false positive and false negative labeling when deploying DCNN based methods.

Fully Convolutional Neural Networks Improve Abdominal Organ Segmentation.

Abdominal image segmentation is a challenging, yet important clinical problem. Variations in body size, position, and relative organ positions greatly complicate the segmentation process. Historically, multi-atlas methods have achieved leading results across imaging modalities and anatomical targets.