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630 results on '"Wang, Huanan"'

3. Shape-recovery of implanted shape-memory devices remotely triggered via image-guided ultrasound heating.

Author

Zhu, Yang, Deng, Kaicheng, Zhou, Jianwei, Lai, Chong, Ma, Zuwei, Zhang, Hua, Pan, Jiazhen, Shen, Liyin, Bucknor, Matthew, Kim, Seungil, Chen, Guangjie, Ye, Sang-Ho, Zhang, Yue, Liu, Donghong, Gao, Changyou, Xu, Yonghua, Wang, Huanan, Wagner, William, and Ozhinsky, Eugene

Abstract

Shape-memory materials hold great potential to impart medical devices with functionalities useful during implantation, locomotion, drug delivery, and removal. However, their clinical translation is limited by a lack of non-invasive and precise methods to trigger and control the shape recovery, especially for devices implanted in deep tissues. In this study, the application of image-guided high-intensity focused ultrasound (HIFU) heating is tested. Magnetic resonance-guided HIFU triggered shape-recovery of a device made of polyurethane urea while monitoring its temperature by magnetic resonance thermometry. Deformation of the polyurethane urea in a live canine bladder (5 cm deep) is achieved with 8 seconds of ultrasound-guided HIFU with millimeter resolution energy focus. Tissue sections show no hyperthermic tissue injury. A conceptual application in ureteral stent shape-recovery reduces removal resistance. In conclusion, image-guided HIFU demonstrates deep energy penetration, safety and speed.

Published
2024

18. IPS300+: a Challenging Multimodal Dataset for Intersection Perception System

Author

Wang, Huanan, Zhang, Xinyu, Li, Jun, Li, Zhiwei, Yang, Lei, Pan, Shuyue, and Deng, Yongqiang

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Due to the high complexity and occlusion, insufficient perception in the crowded urban intersection can be a serious safety risk for both human drivers and autonomous algorithms, whereas CVIS (Cooperative Vehicle Infrastructure System) is a proposed solution for full-participants perception in this scenario. However, the research on roadside multimodal perception is still in its infancy, and there is no open-source dataset for such scenario. Accordingly, this paper fills the gap. Through an IPS (Intersection Perception System) installed at the diagonal of the intersection, this paper proposes a high-quality multimodal dataset for the intersection perception task. The center of the experimental intersection covers an area of 3000m2, and the extended distance reaches 300m, which is typical for CVIS. The first batch of open-source data includes 14198 frames, and each frame has an average of 319.84 labels, which is 9.6 times larger than the most crowded dataset (H3D dataset in 2019) by now. In order to facilitate further study, this dataset tries to keep the label documents consistent with the KITTI dataset, and a standardized benchmark is created for algorithm evaluation. Our dataset is available at: http://www.openmpd.com/column/other_datasets.

Published
2021

23. A biodegradable, flexible photonic patch for in vivo phototherapy

Author

Deng, Kaicheng, Tang, Yao, Xiao, Yan, Zhong, Danni, Zhang, Hua, Fang, Wen, Shen, Liyin, Wang, Zhaochuang, Pan, Jiazhen, Lu, Yuwen, Chen, Changming, Gao, Yun, Jin, Qiao, Zhuang, Lenan, Wan, Hao, Zhuang, Liujing, Wang, Ping, Zhai, Junfeng, Ren, Tanchen, Hu, Qiaoling, Lang, Meidong, Zhang, Yue, Wang, Huanan, Zhou, Min, Gao, Changyou, Zhang, Lei, and Zhu, Yang

Published
2023
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32. A honeybee stinger-inspired self-interlocking microneedle patch and its application in myocardial infarction treatment

Author

Lu, Yuwen, Ren, Tanchen, Zhang, Hua, Jin, Qiao, Shen, Liyin, Shan, Mengqi, Zhao, Xinzhe, Chen, Qichao, Dai, Haoli, Yao, Lin, Xie, Jieqi, Ye, Di, Lin, Tengxiang, Hong, Xiaoqian, Deng, Kaicheng, Shen, Ting, Pan, Jiazhen, Jia, Mengyan, Ling, Jun, Li, Peng, Zhang, Yue, Wang, Huanan, Zhuang, Lenan, Gao, Changyou, Mao, Jifu, and Zhu, Yang

Published
2022
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39. Bio‐Inspired Design of 4D‐Printed Scaffolds Capable of Programmable Multi‐Step Transformations Toward Vascular Reconstruction.

Author

Wang, Qifan, Zhang, Yonggang, Shao, Fei, Yang, Xueying, Wang, Shuya, Shen, Yi, and Wang, Huanan

Subjects
TISSUE scaffolds, REGENERATIVE medicine, PHYSIOLOGICAL adaptation, ENDOTHELIAL cells, BLOOD vessels, POLYCAPROLACTONE
Abstract

Many withered leaves or flowers spontaneously curl and transform from flattened structures into tubular constructs upon dehydration. Inspired by this phenomenon, an innovative strategy is developed to design stimuli‐responsive scaffolds that are capable of programmable transformation from flattened 2D constructs into various curled 3D tissue‐mimicking structures. Specifically, Janus‐structured scaffolds consisting of a passive layer of polycaprolactone and an active, triggered‐transforming layer of methacrylate gelatin/alginate hydrogel are 3D‐printed, which replicate the asymmetrical structure of leaves and enabled on‐demand transformation into desired curvatures and shapes through mimicking the regulation mechanism of leaves curling behavior by mesophyll matrix stiffness and vein patterns. Particularly for vascular reconstruction, multi‐step transformation scaffolds capable of primary 2D‐to‐3D transformation into tubular constructs induced by dehydration, and secondary transformation to adapt to the local intravascular geometry in vivo, are successfully developed. In addition, the oriented polycaprolactone layer of the printed scaffold can enable the adhesion, proliferation, and orientation of endothelial cells, thus demonstrating a novel strategy for the design of tissue‐engineered blood vessels. In general, this study provides an innovative design strategy for programmable biomaterials capable of shape morphing and adaptation in physiological conditions, thereby opening up a new avenue for the design of intelligent biomaterials in regenerative medicine. [ABSTRACT FROM AUTHOR]

Published
2024
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45. Si-GAIS: Siamese Generalizable-Attention Instance Segmentation for Intersection Perception System

Author

Wang, Huanan, Zhang, Xinyu, Wang, Hong, and Jun, Li

Abstract

Instance segmentation of traffic participants using vision-based techniques serves as a cornerstone for numerous intelligent transportation systems. Although existing deep learning-based methods have made significant advancements in this field, these algorithms still present considerable challenges with regard to generalizability for commercial deployment. Specifically, the mean average perception (mAP) of these algorithms degrades rapidly when dealing with intersections that are not included in the training set. To address this limitation, a novel instance segmentation approach named Si-GAIS is proposed, which incorporates a siamese structure for the first time with the proposed Generalizable-Attention Encoder (GA). Through the proposed Foreground-Background Fusion Unit (FBF) within GA, efficient feature-level fusion for the foreground and background images is achieved. Additionally, the Interpretable Attention Neck (IA) in GA enables the feature encoder to focus exclusively on the foreground traffic participants while ignoring various backgrounds. To utilize Si-GAIS, an unsupervised method named P-DBSCAN is proposed to obtain high-quality background image for each intersection with slow-moving traffic and camera jitters. Finally, the first multi-intersection multi-category instance segmentation datasets named RopeIns is proposed for validation. Si-GAIS achieves a 7.7% mAP (All APs used in this paper are abbreviations of AP $_{\textit {50}}$ the same with PASCAL VOC.) accuracy improvement compared to the state-of-the-art (SOTA) methods while using fewer parameters, with only a 6.4% decline in AP for car segmentation in unseen intersections and weather conditions, whereas all other SOTA methods decline more than 10%. The proposed dataset and source code are publicly available at https://github.com/441599828/SiGAIS and we hope Si-GAIS will be a new baseline for IPS instance segmentation research.

Published
2024
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