Aortic 3D Deformation Reconstruction

University of Technology Sydney. Faculty of Engineering and Information Technology.Endovascular intervention plays an important role for treating peripheral arterial diseases. As a minimal invasive surgery treatment, the endovascular intervention provides an alternative to the open surgery with smaller incisions. However, it also brings challenges to the surgeons as the surgical catheters need to be manipulated precisely inside patient's artery. Clinical endovascular interventions typically rely on X-ray fluoroscopy to provide a live 2D view for catheter manipulation. However, this 2D view cannot fully reflect the vessel's 3D shape as the information alone one dimension cannot be visualized. Although a 3D model reflecting aortic 3D shape can be obtained from the pre-operative CT imaging, it cannot be used as an intra-operative guidance since the aorta deforms during the operation. Therefore, an accurate visualization of aortic 3D shape is helpful. The aim of our research is to study the deformation reconstruction techniques and develop efficient frameworks to recover aortic 3D shape intra-operatively. In our first research, an initial aortic 3D deformation reconstruction framework is proposed. As the X-ray images may contain many partial occlusions and background artefacts, semantic features (aortic contour pixels) are used to calculate the 2D-3D non-rigid correspondence between the model vertices and the feature pixels. Having the correspondence, the deformation estimation is formulated as a non-linear optimization problem that can be solved iteratively using Gauss-Newton method. In our second research, we improve our initial framework from two aspects. First, the feature selection process is performed according to a deep learning based image segmentation. This makes our intra-operative reconstruction fully automatic. Second, a signed distance field based correspondence is used. This helps increase the calculation speed of the vertex-feature non-rigid correspondence. The accuracy of our framework is further improved in our third research. The main idea is to combine the aortic centreline reconstruction together with the vessel's shape reconstruction. We first reconstruct the vessel’s 3D centreline based on the 2D centreline features extracted from the X-ray images. Using the reconstructed centreline, the vessel's 3D shape is initially reconstructed. This initial shape reconstruction is used as the input model for a final aortic deformation reconstruction. Since the initial shape is close to the final result, the vertex-feature matching is greatly improved, which results in a more robust reconstruction of aortic 3D deformation. Detailed real phantom experiments are conducted for all the proposed frameworks, and the results demonstrate the reconstruction accuracy

Base excision repair of oxidative DNA damage coupled with removal of a CAG repeat hairpin attenuates trinucleotide repeat expansion

Trinucleotide repeat (TNR) expansion is responsible for numerous human neurodegenerative diseases. However, the underlying mechanisms remain unclear. Recent studies have shown that DNA base excision repair (BER) can mediate TNR expansion and deletion by removing base lesions in different locations of a TNR tract, indicating that BER can promote or prevent TNR expansion in a damage location–dependent manner. In this study, we provide the first evidence that the repair of a DNA base lesion located in the loop region of a CAG repeat hairpin can remove the hairpin, attenuating repeat expansion. We found that an 8-oxoguanine located in the loop region of CAG hairpins of varying sizes was removed by OGG1 leaving an abasic site that was subsequently 5′-incised by AP endonuclease 1, introducing a single-strand breakage in the hairpin loop. This converted the hairpin into a double-flap intermediate with a 5′- and 3′-flap that was cleaved by flap endonuclease 1 and a 3′-5′ endonuclease Mus81/Eme1, resulting in complete or partial removal of the CAG hairpin. This further resulted in prevention and attenuation of repeat expansion. Our results demonstrate that TNR expansion can be prevented via BER in hairpin loops that is coupled with the removal of TNR hairpins

Satellite Image Based Cross-view Localization for Autonomous Vehicle

Existing spatial localization techniques for autonomous vehicles mostly use a pre-built 3D-HD map, often constructed using a survey-grade 3D mapping vehicle, which is not only expensive but also laborious. This paper shows that by using an off-the-shelf high-definition satellite image as a ready-to-use map, we are able to achieve cross-view vehicle localization up to a satisfactory accuracy, providing a cheaper and more practical way for localization. While the utilization of satellite imagery for cross-view localization is an established concept, the conventional methodology focuses primarily on image retrieval. This paper introduces a novel approach to cross-view localization that departs from the conventional image retrieval method. Specifically, our method develops (1) a Geometric-align Feature Extractor (GaFE) that leverages measured 3D points to bridge the geometric gap between ground and overhead views, (2) a Pose Aware Branch (PAB) adopting a triplet loss to encourage pose-aware feature extraction, and (3) a Recursive Pose Refine Branch (RPRB) using the Levenberg-Marquardt (LM) algorithm to align the initial pose towards the true vehicle pose iteratively. Our method is validated on KITTI and Ford Multi-AV Seasonal datasets as ground view and Google Maps as the satellite view. The results demonstrate the superiority of our method in cross-view localization with median spatial and angular errors within $1$ meter and $1^\circ$, respectively.Comment: Accepted by ICRA202

An Alternative to WSSS? An Empirical Study of the Segment Anything Model (SAM) on Weakly-Supervised Semantic Segmentation Problems

The Segment Anything Model (SAM) has demonstrated exceptional performance and versatility, making it a promising tool for various related tasks. In this report, we explore the application of SAM in Weakly-Supervised Semantic Segmentation (WSSS). Particularly, we adapt SAM as the pseudo-label generation pipeline given only the image-level class labels. While we observed impressive results in most cases, we also identify certain limitations. Our study includes performance evaluations on PASCAL VOC and MS-COCO, where we achieved remarkable improvements over the latest state-of-the-art methods on both datasets. We anticipate that this report encourages further explorations of adopting SAM in WSSS, as well as wider real-world applications.Comment: Technique repor

Recent progress in Ti-based nanocomposite anodes for lithium ion batteries

Studying on the anode materials with high energy densities for next-generation lithium-ion batteries (LIBs) is the key for the wide application for electrochemical energy storage devices. Ti-based compounds as promising anode materials are known for their outstanding high-rate capacity and cycling stability as well as improved safety over graphite. However, Ti-based materials still suffer from the low capacity, thus largely limiting their commercialized application. Here, we present an overview of the recent development of Ti-based anode materials in LIBs, and special emphasis is placed on capacity enhancement by rational design of hybrid nanocomposites with conversion-/ alloying-type anodes. This review is expected to provide a guidance for designing novel Ti-based materials for energy storage and conversion. Keywords: lithium-ion batteries (LIBs) anode titania lithium titanateNational Natural Science Foundation (China) (51472137)National Natural Science Foundation (China) (51772163

Spatiotemporal Self-supervised Learning for Point Clouds in the Wild

Self-supervised learning (SSL) has the potential to benefit many applications, particularly those where manually annotating data is cumbersome. One such situation is the semantic segmentation of point clouds. In this context, existing methods employ contrastive learning strategies and define positive pairs by performing various augmentation of point clusters in a single frame. As such, these methods do not exploit the temporal nature of LiDAR data. In this paper, we introduce an SSL strategy that leverages positive pairs in both the spatial and temporal domain. To this end, we design (i) a point-to-cluster learning strategy that aggregates spatial information to distinguish objects; and (ii) a cluster-to-cluster learning strategy based on unsupervised object tracking that exploits temporal correspondences. We demonstrate the benefits of our approach via extensive experiments performed by self-supervised training on two large-scale LiDAR datasets and transferring the resulting models to other point cloud segmentation benchmarks. Our results evidence that our method outperforms the state-of-the-art point cloud SSL methods.Comment: CVPR accepte