Medical Image Computing and Computer-Assisted Intervention

We have performed vitual screening of 1000 ligands and identified 9 compounds that can potentially inhibit SARS-CoV-2 3CLpro This docking study is further complemented by the MD/MMGBSA study br

https://pubs.rsna.org/doi/pdf/10.1148/radiol.2020200241

Chest CT Findings in 2019 Novel Coronavirus (2019-nCoV) Infections from Wuhan, China: Key Points for the Radiologist.

Non-point source (NPS) pollution has been increasingly serious in China since the 1990s. The increases of agricultural NPS pollution in China is evaluated for the period 2000-2008 by surveying the literature on water and soil pollution from fertilizers and pesticides, and assessing the surplus nitrogen balance within provinces. The main causes for NPS pollution were excessive inputs of nitrogen fertilizer and pesticides, which were partly the result of the inadequate agricultural extension services and the rapid expansion of intensive livestock production with little of waste management. The annual application of synthetic nitrogen fertilizers and pesticides in China increased by 50.7 and 119.7%, respectively, during 1991-2008. The mitigation measures to reduce NPS pollution include: correct distortion in fertilizer prices; improve incentives for the recycling of organic manure; provide farmers with better information on the sound use of agro-chemicals; and tighten the regulations and national standards on organic waste disposal and pesticides use.

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Agricultural non-point source pollution in China: causes and mitigation measures.

The semantic image segmentation task consists of classifying each pixel of an image into an instance, where each instance corresponds to a class. This task is a part of the concept of scene understanding or better explaining the global context of an image. In the medical image analysis domain, image segmentation can be used for image-guided interventions, radiotherapy, or improved radiological diagnostics. In this review, we categorize the leading deep learning-based medical and non-medical image segmentation solutions into six main groups of deep architectural, data synthesis-based, loss function-based, sequenced models, weakly supervised, and multi-task methods and provide a comprehensive review of the contributions in each of these groups. Further, for each group, we analyze each variant of these groups and discuss the limitations of the current approaches and present potential future research directions for semantic image segmentation.

Deep semantic segmentation of natural and medical images: a review

Although numerous studies suggest that organic amendments are better at maintaining soil fertility and crop production than mineral-only fertilization, it is unclear if this occurs in different agricultural systems on a global scale. Here we report a comprehensive meta-analysis of 690 independent experiments comparing the performance of organic amendments and mineral-only fertilization on crop yields, the soil organic carbon (SOC) and total nitrogen (TN) contents, soil nutrient dynamics and biological properties. Our analysis shows that organic amendments increased crop yields on average of 27% than mineral-only fertilization. Farmyard manure (FYM) had the highest effect (49% increase) and this was especially clear in wheat croplands (40% increase). Organic amendment increased the amount of SOC (38%), TN (20%), microbial biomass carbon (MBC; 51%) and microbial biomass nitrogen (MBN; 24%) than mineral-only fertilization. Organic amendments also increased the soil microbiome enzyme activity in terms of soil hydrolytic C acquisition (C-acq; 39%), N acquisition (N-acq; 22%), P acquisition (P-acq; 48%) and oxidative decomposition (OX; 58%). Increased nutrient acquisition and oxidative decomposition could explain the positive effects of organic amendment on crop yields. These observed patterns were consistent for most organic amendments and cropping systems in diverse regions of the world. In summary, our analysis suggests that organic amendments can improve microbe-mediated soil ecosystem functioning, long-term soil fertility and crop productivity, relative to mineral fertilization, on a global scale.

Organic amendments increase crop yields by improving microbe-mediated soil functioning of agroecosystems: A meta-analysis

Semantic segmentation of surgical instruments plays a crucial role in robot-assisted surgery. However, accurate segmentation of cataract surgical instruments is still a challenge due to specular reflection and class imbalance issues. In this paper, an attention-guided network is proposed to segment the cataract surgical instrument. A new attention module is designed to learn discriminative features and address the specular reflection issue. It captures global context and encodes semantic dependencies to emphasize key semantic features, boosting the feature representation. This attention module has very few parameters, which helps to save memory. Thus, it can be flexibly plugged into other networks. Besides, a hybrid loss is introduced to train our network for addressing the class imbalance issue, which merges cross entropy and logarithms of Dice loss. A new dataset named Cata7 is constructed to evaluate our network. To the best of our knowledge, this is the first cataract surgical instrument dataset for semantic segmentation. Based on this dataset, RAUNet achieves state-of-the-art performance 97.71\(\%\) mean Dice and 95.62\(\%\) mean IOU.

RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments

Segmentation for tracking surgical instruments plays an important role in robot-assisted surgery. Segmentation of surgical instruments contributes to capturing accurate spatial information for tracking. In this paper, a novel network, Refined Attention Segmentation Network, is proposed to simultaneously segment surgical instruments and identify their categories. The U-shape network which is popular in segmentation is used. Different from previous work, an attention module is adopted to help the network focus on key regions, which can improve the segmentation accuracy. To solve the class imbalance problem, the weighted sum of the cross entropy loss and the logarithm of the Jaccard index is used as loss function. Furthermore, transfer learning is adopted in our network. The encoder is pre-trained on ImageNet. The dataset from the MICCAI EndoVis Challenge 2017 is used to evaluate our network. Based on this dataset, our network achieves state-of-the-art performance 94.65% mean Dice and 90.33% mean IOU.

RASNet: Segmentation for Tracking Surgical Instruments in Surgical Videos Using Refined Attention Segmentation Network

Geographical and climatic differences in long-term effect of organic and inorganic amendments on soil enzymatic activities and respiration in field experimental stations of China

The percutaneous coronary interventions (PCI) require complex operating skills of the interventional devices and make the surgeons being exposed to heavy X-ray radiation. Accurate delivery of the interventional devices and avoiding the radiation are especially important for the surgeons. This paper presents a novel dedicated dual-finger robotic hand (DRH) and a console to assist the surgeons to deliver the interventional devices in PCIs. The system is designed in the master-slave way which helps the surgeons to reduce the exposure to radiation. The mechanism of the DRH is bio-inspired and motions are decoupled in kinematics. In PCI procedures, the accuracy of the guidewire delivery and the catheter tip placement have significant effects on the surgical results. The performances of the DRH in delivering the guidewire and the balloon/stent catheter were evaluated by three surgical manipulations. The results show that the DRH has the ability to deliver the guidewire and the balloon/stent catheter precisely.

Design and evaluation of a bio-inspired robotic hand for percutaneous coronary intervention

The real-time segmentation of surgical instruments plays a crucial role in robot-assisted surgery. However, it is still a challenging task to implement deep learning models to do real-time segmentation for surgical instruments due to their high computational costs and slow inference speed. In this paper, we propose an attention-guided lightweight network (LWANet), which can segment surgical instruments in real-time. LWANet adopts encoder-decoder architecture, where the encoder is the lightweight network MobileNetV2, and the decoder consists of depthwise separable convolution, attention fusion block, and transposed convolution. Depthwise separable convolution is used as the basic unit to construct the decoder, which can reduce the model size and computational costs. Attention fusion block captures global contexts and encodes semantic dependencies between channels to emphasize target regions, contributing to locating the surgical instrument. Transposed convolution is performed to upsample feature maps for acquiring refined edges. LWANet can segment surgical instruments in real-time while takes little computational costs. Based on 960x544 inputs, its inference speed can reach 39 fps with only 3.39 GFLOPs. Also, it has a small model size and the number of parameters is only 2.06 M. The proposed network is evaluated on two datasets. It achieves state-of-the- art performance 94.10% mean IOU on Cata7 and obtains a new record on EndoVis 2017 with a 4.10% increase on mean IOU.

Xiao-Liang Xie

Papers

RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments

RASNet: Segmentation for Tracking Surgical Instruments in Surgical Videos Using Refined Attention Segmentation Network

Geographical and climatic differences in long-term effect of organic and inorganic amendments on soil enzymatic activities and respiration in field experimental stations of China

Design and evaluation of a bio-inspired robotic hand for percutaneous coronary intervention

Attention-Guided Lightweight Network for Real-Time Segmentation of Robotic Surgical Instruments