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Fully-supervised airway segmentation has accomplished significant triumphs over the years in aiding pre-operative diagnosis and intra-operative navigation. However, full voxel-level annotation constitutes a labor-intensive and time-consuming task, often plagued by issues such as missing branches, branch annotation discontinuity, or erroneous edge delineation. label-efficient solutions for airway extraction are rarely explored yet primarily demanding in medical practice. To this end, we introduce a novel skeleton-level annotation (SkA) tailored to the airway, which simplifies the annotation workflow while enhancing annotation consistency and accuracy, preserving the complete topology. Furthermore, we propose a skeleton-supervised learning framework to achieve accurate airway segmentation. Firstly, a dual-stream buffer inference is introduced to realize initial label propagation from SkA, avoiding the collapse of direct learning from SkA. Then, we construct a geometry-aware dual-path propagation framework (GDP) to further promote complementary propagation learning, composed of hard geometry-aware propagation learning and soft geometry-aware propagation guidance. Experiments reveal that our proposed framework outperforms the competing methods with SKA, which amounts to only 1.96% airways, and achieves comparable performance with the baseline model that is fully supervised with 100% airways, demonstrating its significant potential in achieving label-efficient segmentation for other tubular structures, such as vessels.

Airway segmentation, especially bronchioles segmentation, is an important but challenging task because distal bronchus are sparsely distributed and of a fine scale. Existing neural networks usually exploit sparse topology to learn the connectivity of bronchioles and inefficient shallow features to capture such high-frequency information, leading to the breakage or missed detection of individual thin branches. To address these problems, we contribute a new bronchial segmentation method based on Group Deep Dense Supervision (GDDS) that emphasizes fine-scale bronchioles segmentation in a simple-but-effective manner. First, Deep Dense Supervision (DDS) is proposed by constructing local dense topology skillfully and implementing dense topological learning on a specific shallow feature layer. GDDS further empowers the shallow features with better perception ability to detect bronchioles, even the ones that are not easily discernible to the naked eye. Extensive experiments on the BAS benchmark dataset have shown that our method promotes the network to have a high sensitivity in capturing fine-scale branches and outperforms state-of-the-art methods by a large margin (+12.8 % in BD and +8.8 % in TD) while only introducing a small number of extra parameters., Comment: Submitted to MICCAI2023

The past one and a half decades have witnessed the tremendous progress of two-dimensional (2D) crystals, including graphene, transition-metal dichalcogenides, black phosphorus, MXenes, hexagonal boron nitride, etc., in a variety of fields. The key to their success is their unique structural, electrical, mechanical and optical properties. Herein, this paper gives a comprehensive summary on the recent advances in 2D materials for optoelectronic approaches with the emphasis on the morphology and structure, optical properties, synthesis methods, as well as detailed optoelectronic applications. Additionally, the challenges and perspectives in the current development of 2D materials are also summarized and indicated. Therefore, this review can provide a reference for further explorations and innovations of 2D material-based optoelectronics devices.

The past one and a half decades have witnessed the tremendous progress of two-dimensional (2D) crystals, including graphene, transition-metal dichalcogenides, black phosphorus, MXenes, hexagonal boron nitride, etc., in a variety of fields. The key to their success is their unique structural, electrical, mechanical and optical properties. Herein, this paper gives a comprehensive summary on the recent advances in 2D materials for optoelectronic approaches with the emphasis on the morphology and structure, optical properties, synthesis methods, as well as detailed optoelectronic applications. Additionally, the challenges and perspectives in the current development of 2D materials are also summarized and indicated. Therefore, this review can provide a reference for further explorations and innovations of 2D material-based optoelectronics devices.

The past one and a half decades have witnessed the tremendous progress of two-dimensional (2D) crystals, including graphene, transition-metal dichalcogenides, black phosphorus, MXenes, hexagonal boron nitride, etc., in a variety of fields. The key to their success is their unique structural, electrical, mechanical and optical properties. Herein, this paper gives a comprehensive summary on the recent advances in 2D materials for optoelectronic approaches with the emphasis on the morphology and structure, optical properties, synthesis methods, as well as detailed optoelectronic applications. Additionally, the challenges and perspectives in the current development of 2D materials are also summarized and indicated. Therefore, this review can provide a reference for further explorations and innovations of 2D material-based optoelectronics devices.

The past one and a half decades have witnessed the tremendous progress of two-dimensional (2D) crystals, including graphene, transition-metal dichalcogenides, black phosphorus, MXenes, hexagonal boron nitride, etc., in a variety of fields. The key to their success is their unique structural, electrical, mechanical and optical properties. Herein, this paper gives a comprehensive summary on the recent advances in 2D materials for optoelectronic approaches with the emphasis on the morphology and structure, optical properties, synthesis methods, as well as detailed optoelectronic applications. Additionally, the challenges and perspectives in the current development of 2D materials are also summarized and indicated. Therefore, this review can provide a reference for further explorations and innovations of 2D material-based optoelectronics devices.

The past one and a half decades have witnessed the tremendous progress of two-dimensional (2D) crystals, including graphene, transition-metal dichalcogenides, black phosphorus, MXenes, hexagonal boron nitride, etc., in a variety of fields. The key to their success is their unique structural, electrical, mechanical and optical properties. Herein, this paper gives a comprehensive summary on the recent advances in 2D materials for optoelectronic approaches with the emphasis on the morphology and structure, optical properties, synthesis methods, as well as detailed optoelectronic applications. Additionally, the challenges and perspectives in the current development of 2D materials are also summarized and indicated. Therefore, this review can provide a reference for further explorations and innovations of 2D material-based optoelectronics devices.

John Alimamy Kabba,1– 3 Peter Bai James,4,5 Zongjie Li,1– 3 Christian Hanson,6,7 Jie Chang,1– 3 Chenai Kitchen,1– 3 Minghuan Jiang,1– 3 Mingyue Zhao,1– 3 Caijun Yang,1– 3 Yu Fang1– 3 1Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmacy, Xi’an Jiaotong University, Xi’an, People’s Republic of China; 2Center for Drug Safety and Policy Research, Xi’an Jiaotong University, Xi’an, People’s Republic of China; 3Shaanxi Center for Health Reform and Development Research, Xi’an, People’s Republic of China; 4Faculty of Pharmaceutical Sciences, College of Medicine and Allied Health Sciences, University of Sierra Leone, Freetown, Sierra Leone; 5Australian Research Centre in Complementary and Integrative Medicine, Faculty of Health, University of Technology Sydney, Sydney 2007, Australia; 6Pharmacy Board, Ministry of Health and Sanitation New England, Freetown, Sierra Leone; 7Pharmacy Department, Well Star Atlanta Medical Centre South, East Point, GA, USACorrespondence: Yu FangDepartment of Pharmacy Administration and Clinical Pharmacy, Xi’an Jiaotong University, #76 Yanta West Road, Xi’an 710061, People’s Republic of ChinaTel +86 29 82655132Email yufang@mail.xjtu.edu.cnPurpose: Rational use of medicines is a necessary constrict towards increasing access for those that desperately need them in society. In this study, we assess medicines prescribing patterns in healthcare facilities implementing free healthcare policy for pregnant women, lactating mothers and children under the age of five in Sierra Leone.Materials and Methods: Using WHO drug use indicators, we evaluated prescription records from the pharmacies of four hospitals; one from each of the four regions in Sierra Leone. To study prescribing indicators, we systematically sampled 1200 prescriptions overall (30

Glioma is one of the most refractory types of brain tumor. Accurate tumor boundary identification and complete resection of the tumor are essential for glioma removal during brain surgery. We present a method based on visible resonance Raman (VRR) spectroscopy to identify glioma margins and grades. A set of diagnostic spectral biomarkers features are presented based on tissue composition changes revealed by VRR. The Raman spectra include molecular vibrational fingerprints of carotenoids, tryptophan, amide I/II/III, proteins, and lipids. These basic in situ spectral biomarkers are used to identify the tissue from the interface between brain cancer and normal tissue and to evaluate glioma grades. The VRR spectra are also analyzed using principal component analysis for dimension reduction and feature detection and support vector machine for classification. The cross-validated sensitivity, specificity, and accuracy are found to be 100%, 96.3%, and 99.6% to distinguish glioma tissues from normal brain tissues, respectively. The area under the receiver operating characteristic curve for the classification is about 1.0. The accuracies to distinguish normal, low grade (grades I and II), and high grade (grades III and IV) gliomas are found to be 96.3%, 53.7%, and 84.1% for the three groups, respectively, along with a total accuracy of 75.1%. A set of criteria for differentiating normal human brain tissues from normal control tissues is proposed and used to identify brain cancer margins, yielding a diagnostic sensitivity of 100% and specificity of 71%. Our study demonstrates the potential of VRR as a label-free optical molecular histopathology method used for in situ boundary line judgment for brain surgery in the margins.

Glioma is one of the most refractory types of brain tumor. Accurate tumor boundary identification and complete resection of the tumor are essential for glioma removal during brain surgery. We present a method based on visible resonance Raman (VRR) spectroscopy to identify glioma margins and grades. A set of diagnostic spectral biomarkers features are presented based on tissue composition changes revealed by VRR. The Raman spectra include molecular vibrational fingerprints of carotenoids, tryptophan, amide I/II/III, proteins, and lipids. These basic in situ spectral biomarkers are used to identify the tissue from the interface between brain cancer and normal tissue and to evaluate glioma grades. The VRR spectra are also analyzed using principal component analysis for dimension reduction and feature detection and support vector machine for classification. The cross-validated sensitivity, specificity, and accuracy are found to be 100%, 96.3%, and 99.6% to distinguish glioma tissues from normal brain tissues, respectively. The area under the receiver operating characteristic curve for the classification is about 1.0. The accuracies to distinguish normal, low grade (grades I and II), and high grade (grades III and IV) gliomas are found to be 96.3%, 53.7%, and 84.1% for the three groups, respectively, along with a total accuracy of 75.1%. A set of criteria for differentiating normal human brain tissues from normal control tissues is proposed and used to identify brain cancer margins, yielding a diagnostic sensitivity of 100% and specificity of 71%. Our study demonstrates the potential of VRR as a label-free optical molecular histopathology method used for in situ boundary line judgment for brain surgery in the margins.