This paper presents a new supervised method for vessel segmentation in retinal images. This method remolds the task of segmentation as a problem of cross-modality data transformation from retinal image to vessel map. A wide and deep neural network with strong induction ability is proposed to model the transformation, and an efficient training strategy is presented. Instead of a single label of the center pixel, the network can output the label map of all pixels for a given image patch. Our approach outperforms reported state-of-the-art methods in terms of sensitivity, specificity and accuracy. The result of cross-training evaluation indicates its robustness to the training set. The approach needs no artificially designed feature and no preprocessing step, reducing the impact of subjective factors. The proposed method has the potential for application in image diagnosis of ophthalmologic diseases, and it may provide a new, general, high-performance computing framework for image segmentation.

InGaAs is an important bandgap-variable ternary semiconductor which has wide applications in electronics and optoelectronics. In this work, single-crystal InGaAs nanowires were synthesized by a chemical vapor deposition method. Photoluminescence measurements indicate the InGaAs nanowires have strong light emission in near-infrared region. For the first time, photodetector based on as-grown InGaAs nanowires was also constructed. It shows good light response over a broad spectral range in infrared region with responsivity of 6.5 × 103 A W−1 and external quantum efficiency of 5.04 × 105 %. This photodetector may have potential applications in integrated optoelectronic devices and systems.Electronic supplementary materialThe online version of this article (doi:10.1007/s40820-015-0058-0) contains supplementary material, which is available to authorized users.

Purpose In this study, we proposed an automated deep learning (DL) method for head and neck cancer (HNC) gross tumor volume (GTV) contouring on positron emission tomography-computed tomography (PET-CT) images. Materials and Methods PET-CT images were collected from 22 newly diagnosed HNC patients, of whom 17 (Database 1) and 5 (Database 2) were from two centers, respectively. An oncologist and a radiologist decided the gold standard of GTV manually by consensus. We developed a deep convolutional neural network (DCNN) and trained the network based on the two-dimensional PET-CT images and the gold standard of GTV in the training dataset. We did two experiments: Experiment 1, with Database 1 only, and Experiment 2, with both Databases 1 and 2. In both Experiment 1 and Experiment 2, we evaluated the proposed method using a leave-one-out cross-validation strategy. We compared the median results in Experiment 2 (GTVa) with the performance of other methods in the literature and with the gold standard (GTVm). Results A tumor segmentation task for a patient on coregistered PET-CT images took less than one minute. The dice similarity coefficient (DSC) of the proposed method in Experiment 1 and Experiment 2 was 0.481∼0.872 and 0.482∼0.868, respectively. The DSC of GTVa was better than that in previous studies. A high correlation was found between GTVa and GTVm (R = 0.99, P < 0.001). The median volume difference (%) between GTVm and GTVa was 10.9%. The median values of DSC, sensitivity, and precision of GTVa were 0.785, 0.764, and 0.789, respectively. Conclusion A fully automatic GTV contouring method for HNC based on DCNN and PET-CT from dual centers has been successfully proposed with high accuracy and efficiency. Our proposed method is of help to the clinicians in HNC management.

Single-nanowire photodetectors have potential applications in integrated optoelectronic devices and systems. Here, bandgap-engineered GaAs 0.26 Sb 0.74 alloy nanowires were synthesized via a chemical vapor deposition method. The synthesized nanowires are single crystals grown along the [111] B direction with length up to 50 μm and diameter ranging from 40 to 500 nm. Photodetectors are built based on these single-alloy nanowires, which show a wide response in the near-infrared region with a high response peak located in the optical communication region (1.31 μm), as well as an external quantum efficiency of 1.62×10 5 %, a responsivity of 1.7×10 3 A W −1 and a short response time of 60 ms. These novel near-infrared photodetectors may find promising potential applications in integrated infrared photodetection, thermal imaging, information communication and processing.

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