Author Correction: U-Net: deep learning for cell counting, detection, and morphometry.

In the version of this paper originally published, one of the affiliations for Dominic Mai was incorrect: "Center for Biological Systems Analysis (ZBSA), Albert-Ludwigs-University, Freiburg, Germany" should have been "Life Imaging Center, Center for Biological Systems Analysis, Albert-Ludwigs-University, Freiburg, Germany." This change required some renumbering of subsequent author affiliations. These corrections have been made in the PDF and HTML versions of the article, as well as in any cover sheets for associated Supplementary Information.

U-Net: deep learning for cell counting, detection, and morphometry.

U-Net is a generic deep-learning solution for frequently occurring quantification tasks such as cell detection and shape measurements in biomedical image data. We present an ImageJ plugin that enables non-machine-learning experts to analyze their data with U-Net on either a local computer or a remote server/cloud service. The plugin comes with pretrained models for single-cell segmentation and allows for U-Net to be adapted to new tasks on the basis of a few annotated samples.

Polymer Nanoreactors Shield Perovskite Nanocrystals from Degradation.

Halide perovskite nanocrystals (NCs) have shown impressive advances, exhibiting optical properties that outpace conventional semiconductor NCs, such as near-unity quantum yields and ultrafast radiative decay rates. Nevertheless, the NCs suffer even more from stability problems at ambient conditions and due to moisture than their bulk counterparts. Herein, we report a strategy of employing polymer micelles as nanoreactors for the synthesis of methylammonium lead trihalide perovskite NCs. Encapsulated by this polymer shell, the NCs display strong stability against water degradation and halide ion migration. Thin films comprising these NCs exhibit a more than 15-fold increase in lifespan in comparison to unprotected NCs in ambient conditions and even survive over 75 days of complete immersion in water. Furthermore, the NCs, which exhibit quantum yields of up to 63% and tunability of the emission wavelength throughout the visible range, show no signs of halide ion exchange. Additionally, heterostructures of MAPI and MAPBr NC layers exhibit efficient Förster resonance energy transfer (FRET), revealing a strategy for optoelectronic integration.

Confining metal-halide perovskites in nanoporous thin films.

Controlling the size and shape of semiconducting nanocrystals advances nanoelectronics and photonics. Quantum-confined, inexpensive, solution-derived metal halide perovskites offer narrowband, color-pure emitters as integral parts of next-generation displays and optoelectronic devices. We use nanoporous silicon and alumina thin films as templates for the growth of perovskite nanocrystallites directly within device-relevant architectures without the use of colloidal stabilization. We find significantly blue-shifted photoluminescence emission by reducing the pore size; normally infrared-emitting materials become visibly red, and green-emitting materials become cyan and blue. Confining perovskite nanocrystals within porous oxide thin films drastically increases photoluminescence stability because the templates auspiciously serve as encapsulation. We quantify the template-induced size of the perovskite crystals in nanoporous silicon with microfocus high-energy x-ray depth profiling in transmission geometry, verifying the growth of perovskite nanocrystals throughout the entire thickness of the nanoporous films. Low-voltage electroluminescent diodes with narrow, blue-shifted emission fabricated from nanocrystalline perovskites grown in embedded nanoporous alumina thin films substantiate our general concept for next-generation photonic devices.

Gland segmentation in colon histology images: The glas challenge contest.

Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer. In clinical practice, the morphology of intestinal glands, including architectural appearance and glandular formation, is used by pathologists to inform prognosis and plan the treatment of individual patients. However, achieving good inter-observer as well as intra-observer reproducibility of cancer grading is still a major challenge in modern pathology. An automated approach which quantifies the morphology of glands is a solution to the problem. This paper provides an overview to the Gland Segmentation in Colon Histology Images Challenge Contest (GlaS) held at MICCAI'2015. Details of the challenge, including organization, dataset and evaluation criteria, are presented, along with the method descriptions and evaluation results from the top performing methods.