Sd-net: a semi-supervised double-cooperative network for liver segmentation from computed tomography (CT) images.

INTRODUCTION: The automatic segmentation of the liver is a crucial step in obtaining quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This task is challenging due to the frequent presence of noise and sampling artifacts in computerized tomography (CT) images, as well as the complex background, variable shapes, and blurry boundaries of the liver. Standard segmentation of medical images based on full-supervised convolutional networks demands accurate dense annotations. Such a learning framework is built on laborious manual annotation with strict requirements for expertise, leading to insufficient high-quality labels. METHODS: To overcome such limitation and exploit massive weakly labeled data, we relaxed the rigid labeling requirement and developed a semi-supervised double-cooperative network (SD- Net). SD-Net is trained to segment the complete liver volume from preoperative abdominal CT images by using limited labeled datasets and large-scale unlabeled datasets. Specifically, to enrich the diversity of unsupervised information, we construct SD-Net consisting of two collaborative network models. Within the supervised training module, we introduce an adaptive mask refinement approach. First, each of the two network models predicts the labeled dataset, after which adaptive mask refinement of the difference predictions is implemented to obtain more accurate liver segmentation results. In the unsupervised training module, a dynamic pseudo-label generation strategy is proposed. First each of the two models predicts unlabeled data and the better prediction is considered as pseudo-labeling before training. RESULTS AND DISCUSSION: Based on the experimental findings, the proposed method achieves a dice score exceeding 94%, indicating its high level of accuracy and its suitability for everyday clinical use.

Upcycling Waste Lard Oil into Vertical Graphene Sheets by Inductively Coupled Plasma Assisted Chemical Vapor Deposition.

Vertical graphene (VG) sheets were single-step synthesized via inductively coupled plasma (ICP)-enhanced chemical vapor deposition (PECVD) using waste lard oil as a sustainable and economical carbon source. Interweaved few-layer VG sheets, H2, and other hydrocarbon gases were obtained after the decomposition of waste lard oil. The influence of parameters such as temperature, gas proportion, ICP power was investigated to tune the nanostructures of obtained VG, which indicated that a proper temperature and H2 concentration was indispensable for the synthesis of VG sheets. Rich defects of VG were formed with a high I D / I G ratio (1.29), consistent with the dense edges structure observed in electron microscopy. Additionally, the morphologies, crystalline degree, and wettability of nanostructure carbon induced by PECVD and ICP separately were comparatively analyzed. The present work demonstrated the potential of our PECVD recipe to synthesize VG from abundant natural waste oil, which paved the way to upgrade the low-value hydrocarbons into advanced carbon material.