Large-scale Point Cloud Registration Based on Graph Matching Optimization

Point Clouds Registration is a fundamental and challenging problem in 3D computer vision. It has been shown that the isometric transformation is an essential property in rigid point cloud registration, but the existing methods only utilize it in the outlier rejection stage. In this paper, we emphasize that the isometric transformation is also important in the feature learning stage for improving registration quality. We propose a \underline{G}raph \underline{M}atching \underline{O}ptimization based \underline{Net}work (denoted as GMONet for short), which utilizes the graph matching method to explicitly exert the isometry preserving constraints in the point feature learning stage to improve %refine the point representation. Specifically, we %use exploit the partial graph matching constraint to enhance the overlap region detection abilities of super points ($i.e.,$ down-sampled key points) and full graph matching to refine the registration accuracy at the fine-level overlap region. Meanwhile, we leverage the mini-batch sampling to improve the efficiency of the full graph matching optimization. Given high discriminative point features in the evaluation stage, we utilize the RANSAC approach to estimate the transformation between the scanned pairs. The proposed method has been evaluated on the 3DMatch/3DLoMatch benchmarks and the KITTI benchmark. The experimental results show that our method achieves competitive performance compared with the existing state-of-the-art baselines

Enhancing Point Annotations with Superpixel and Confidence Learning Guided for Improving Semi-Supervised OCT Fluid Segmentation

Automatic segmentation of fluid in Optical Coherence Tomography (OCT) images is beneficial for ophthalmologists to make an accurate diagnosis. Although semi-supervised OCT fluid segmentation networks enhance their performance by introducing additional unlabeled data, the performance enhancement is limited. To address this, we propose Superpixel and Confident Learning Guide Point Annotations Network (SCLGPA-Net) based on the teacher-student architecture, which can learn OCT fluid segmentation from limited fully-annotated data and abundant point-annotated data. Specifically, we use points to annotate fluid regions in unlabeled OCT images and the Superpixel-Guided Pseudo-Label Generation (SGPLG) module generates pseudo-labels and pixel-level label trust maps from the point annotations. The label trust maps provide an indication of the reliability of the pseudo-labels. Furthermore, we propose the Confident Learning Guided Label Refinement (CLGLR) module identifies error information in the pseudo-labels and leads to further refinement. Experiments on the RETOUCH dataset show that we are able to reduce the need for fully-annotated data by 94.22\%, closing the gap with the best fully supervised baselines to a mean IoU of only 2\%. Furthermore, We constructed a private 2D OCT fluid segmentation dataset for evaluation. Compared with other methods, comprehensive experimental results demonstrate that the proposed method can achieve excellent performance in OCT fluid segmentation.Comment: Submission to BSP

MSE-Nets: Multi-annotated Semi-supervised Ensemble Networks for Improving Segmentation of Medical Image with Ambiguous Boundaries

Medical image segmentation annotations exhibit variations among experts due to the ambiguous boundaries of segmented objects and backgrounds in medical images. Although using multiple annotations for each image in the fully-supervised has been extensively studied for training deep models, obtaining a large amount of multi-annotated data is challenging due to the substantial time and manpower costs required for segmentation annotations, resulting in most images lacking any annotations. To address this, we propose Multi-annotated Semi-supervised Ensemble Networks (MSE-Nets) for learning segmentation from limited multi-annotated and abundant unannotated data. Specifically, we introduce the Network Pairwise Consistency Enhancement (NPCE) module and Multi-Network Pseudo Supervised (MNPS) module to enhance MSE-Nets for the segmentation task by considering two major factors: (1) to optimize the utilization of all accessible multi-annotated data, the NPCE separates (dis)agreement annotations of multi-annotated data at the pixel level and handles agreement and disagreement annotations in different ways, (2) to mitigate the introduction of imprecise pseudo-labels, the MNPS extends the training data by leveraging consistent pseudo-labels from unannotated data. Finally, we improve confidence calibration by averaging the predictions of base networks. Experiments on the ISIC dataset show that we reduced the demand for multi-annotated data by 97.75\% and narrowed the gap with the best fully-supervised baseline to just a Jaccard index of 4\%. Furthermore, compared to other semi-supervised methods that rely only on a single annotation or a combined fusion approach, the comprehensive experimental results on ISIC and RIGA datasets demonstrate the superior performance of our proposed method in medical image segmentation with ambiguous boundaries

Neoadjuvant chemoradiotherapy combined with immunotherapy for locally advanced rectal cancer: A new era for anal preservation

For locally advanced (T3-4/N+M0) rectal cancer (LARC), neoadjuvant chemoradiotherapy (nCRT) followed by total mesorectal excision (TME) is the standard treatment. It was demonstrated to decrease the local recurrence rate and increase the tumor response grade. However, the distant metastasis remains an unresolved issue. And the demand for anus preservation and better quality of life increases in recent years. Radiotherapy and immunotherapy can be supplement to each other and the combination of the two treatments has a good theoretical basis. Recently, multiple clinical trials are ongoing in terms of the combination of nCRT and immunotherapy in LARC. It was reported that these trials achieved promising short-term efficacy in both MSI-H and MSS rectal cancers, which could further improve the rate of clinical complete response (cCR) and pathological complete response (pCR), so that increase the possibility of ‘Watch and Wait (W&W)’ approach. However, the cCR and pCR is not always consistent, which occurs more frequent when nCRT is combined with immunotherapy. Thus, the efficacy evaluation after neoadjuvant therapy is an important issue for patient selection of W&W approach. Evaluating the cCR accurately needs the combination of multiple traditional examinations, new detective methods, such as PET-CT, ctDNA-MRD and various omics studies. And finding accurate biomarkers can help guide the risk stratification and treatment decisions. And large-scale clinical trials need to be performed in the future to demonstrate the surprising efficacy and to explore the long-term prognosis

Identification and characterization of the highly polymorphic locus D14S739 in the Han Chinese population

Aim To systemically select and evaluate short tandem repeats (STRs) on the chromosome 14 and obtain new STR loci as expanded genotyping markers for forensic application. Methods STRs on the chromosome 14 were filtered from Tandem Repeats Database and further selected based on their positions on the chromosome, repeat patterns of the core sequences, sequence homology of the flanking regions, and suitability of flanking regions in primer design. The STR locus with the highest heterozygosity and polymorphism information content (PIC) was selected for further analysis of genetic polymorphism, forensic parameters, and the core sequence. Results Among 26 STR loci selected as candidates, D14S739 had the highest heterozygosity (0.8691) and PIC (0.8432), and showed no deviation from the Hardy-Weinberg equilibrium. 14 alleles were observed, ranging in size from 21 to 34 tetranucleotide units in the core region of (GATA)9-18 (GACA)7-12 GACG (GACA)2 GATA. Paternity testing showed no mutations. Conclusion D14S739 is a highly informative STR locus and could be a suitable genetic marker for forensic applications in the Han Chinese populatio

Automatic segmentation of white matter hyperintensities in routine clinical brain MRI by 2D VB-Net: A large-scale study

White matter hyperintensities (WMH) are imaging manifestations frequently observed in various neurological disorders, yet the clinical application of WMH quantification is limited. In this study, we designed a series of dedicated WMH labeling protocols and proposed a convolutional neural network named 2D VB-Net for the segmentation of WMH and other coexisting intracranial lesions based on a large dataset of 1,045 subjects across various demographics and multiple scanners using 2D thick-slice protocols that are more commonly applied in clinical practice. Using our labeling pipeline, the Dice consistency of the WMH regions manually depicted by two observers was 0.878, which formed a solid basis for the development and evaluation of the automatic segmentation system. The proposed algorithm outperformed other state-of-the-art methods (uResNet, 3D V-Net and Visual Geometry Group network) in the segmentation of WMH and other coexisting intracranial lesions and was well validated on datasets with thick-slice magnetic resonance (MR) images and the 2017 medical image computing and computer assisted intervention WMH Segmentation Challenge dataset (with thin-slice MR images), all showing excellent effectiveness. Furthermore, our method can subclassify WMH to display the WMH distributions and is very lightweight. Additionally, in terms of correlation to visual rating scores, our algorithm showed excellent consistency with the manual delineations and was overall better than those from other competing methods. In conclusion, we developed an automatic WMH quantification framework for multiple application scenarios, exhibiting a promising future in clinical practice

Enhancing the ophthalmic AI assessment with a fundus image quality classifier using local and global attention mechanisms

BackgroundThe assessment of image quality (IQA) plays a pivotal role in the realm of image-based computer-aided diagnosis techniques, with fundus imaging standing as the primary method for the screening and diagnosis of ophthalmic diseases. Conventional studies on fundus IQA tend to rely on simplistic datasets for evaluation, predominantly focusing on either local or global information, rather than a synthesis of both. Moreover, the interpretability of these studies often lacks compelling evidence. In order to address these issues, this study introduces the Local and Global Attention Aggregated Deep Neural Network (LGAANet), an innovative approach that integrates both local and global information for enhanced analysis.MethodsThe LGAANet was developed and validated using a Multi-Source Heterogeneous Fundus (MSHF) database, encompassing a diverse collection of images. This dataset includes 802 color fundus photography (CFP) images (302 from portable cameras), and 500 ultrawide-field (UWF) images from 904 patients with diabetic retinopathy (DR) and glaucoma, as well as healthy individuals. The assessment of image quality was meticulously carried out by a trio of ophthalmologists, leveraging the human visual system as a benchmark. Furthermore, the model employs attention mechanisms and saliency maps to bolster its interpretability.ResultsIn testing with the CFP dataset, LGAANet demonstrated remarkable accuracy in three critical dimensions of image quality (illumination, clarity and contrast based on the characteristics of human visual system, and indicates the potential aspects to improve the image quality), recording scores of 0.947, 0.924, and 0.947, respectively. Similarly, when applied to the UWF dataset, the model achieved accuracies of 0.889, 0.913, and 0.923, respectively. These results underscore the efficacy of LGAANet in distinguishing between varying degrees of image quality with high precision.ConclusionTo our knowledge, LGAANet represents the inaugural algorithm trained on an MSHF dataset specifically for fundus IQA, marking a significant milestone in the advancement of computer-aided diagnosis in ophthalmology. This research significantly contributes to the field, offering a novel methodology for the assessment and interpretation of fundus images in the detection and diagnosis of ocular diseases

Platinum-nickel alloy excavated nano-multipods with hexagonal close-packed structure and superior activity towards hydrogen evolution reaction

铂镍合金在氢析出(HER)、氧还原(ORR)等重要能量转化反应中具有优异催化性质，受到了人们广泛的关注。近日，谢兆雄教授课题组通过简单的溶剂热方法，首次合成出六方晶系的铂镍合金枝状纳米晶，其中每个枝杈结构由六个{11-20}高能晶面裸露的超薄纳米片组装而成。与面心立方晶系铂镍合金相比，亚稳态的六方晶系铂镍合金在HER反应中表现出更加优异的性质。当电流密度为10 mA·cm-2时，其过电位仅有65 mV，同时质量电流密度高达3.03 mA·µgPt-1 (-70 m V vs. RHE)，是目前为止报道的HER催化剂中质量活性最高的，其突出的催化性能主要来源于晶相作用(同质异晶)及大的比表面积。该项工作为发展高催化性能的铂基合金纳米晶提供了新的研究思路。该研究是在谢兆雄教授和蒋亚琪副教授指导下，与傅钢教授共同合作完成。实验部分由博士生曹振明（第一作者）、陈巧丽、沈守宇、卢邦安，硕士生李慧齐以及博士后张嘉伟共同完成，理论计算部分由傅钢教授课题组完成。【Abstract】Crystal phase regulations may endow materials with enhanced or new functionalities. However, syntheses of noble metal-based allomorphic nanomaterials are extremely difficult, and only a few successful examples have been found. Herein, we report the discovery of hexagonal close-packed Pt–Ni alloy, despite the fact that Pt–Ni alloys are typically crystallized in face-centred cubic structures. The hexagonal close-packed Pt–Ni alloy nano-multipods are synthesized via a facile one-pot solvothermal route, where the branches of nano-multipods take the shape of excavated hexagonal prisms assembled by six nanosheets of 2.5nm thickness. The hexagonal close-packed Pt–Ni excavated nano-multipods exhibit superior catalytic property towards the hydrogen evolution reaction in alkaline electrolyte. The overpotential is only 65mV versus reversible hydrogen electrode at a current density of 10 mAcm-2 , and the mass current density reaches 3.03mA µgPt-1 at -70mV versus reversible hydrogen electrode, which outperforms currently reported catalysts to the best of our knowledge.This work was supported by the National Basic Research Program of China (Grant 2015CB932301), the National Natural Science Foundation of China (Grants 21333008, 21603178 and J1030415) and the Natural Science Foundation of Fujian Province of China (No. 2014J01058). 该研究工作得到科技部（批准号：2015CB932301）、国家自然科学基金委（批准号：21333008, 21603178 和 J1030415）和福建省自然科学基金委（No. 2014J01058）的大力资助与支持