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Tengfei Li

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6 papers

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EAAI Journal 2025 Journal Article

Three-dimensional reconstruction image generation of traditional Chinese painting elements

Qiyao Hu
Jingyu Wang
Xianlin Peng
Tengfei Li
Rui Cao

This paper presents a comprehensive pipeline for generating detailed three-dimensional (3D) models from single images of traditional Chinese painting elements. This task is particularly challenging due to the lack of 3D datasets for Chinese paintings and the limited research on their 3D reconstruction. As a result, direct access to multiple views is precluded. We propose a novel method for the 3D reconstruction of Traditional Chinese Painting Elements, termed TCPE-3D, which has three components of the process. The first component is a multi-view synthesis module named One To Six (OTX) - Multi-View Generating (MVG) Module. This module creates six fixed-view images through a series of preprocessing steps. These images are used to generate the Local Light Field Fusion (LLFF) dataset within the Neural Radiance Fields (NeRF) synthesis module. This process leads to the creation of detailed mesh structures in the final Mesh Generation module. Comparison with several state-of-the-art 3D reconstruction methods shows that our framework achieves better visualization results and higher technical specifications. Additionally, it solves the Janus problem encountered by other algorithms for Chinese painting data. Our dataset is available at https: //github. com/LPDLG/3DTCP-Dataset.

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YNIMG Journal 2022 Journal Article

Longitudinal brain atlases of early developing cynomolgus macaques from birth to 48 months of age

Tao Zhong
Jingkuan Wei
Kunhua Wu
Liangjun Chen
Fenqiang Zhao
Yuchen Pei
Ya Wang
Hongjiang Zhang

Longitudinal brain imaging atlases with densely sampled time-points and ancillary anatomical information are of fundamental importance in studying early developmental characteristics of human and non-human primate brains during infancy, which feature extremely dynamic imaging appearance, brain shape and size. However, for non-human primates, which are highly valuable animal models for understanding human brains, the existing brain atlases are mainly developed based on adults or adolescents, denoting a notable lack of temporally densely-sampled atlases covering the dynamic early brain development. To fill this critical gap, in this paper, we construct a comprehensive set of longitudinal brain atlases and associated tissue probability maps (gray matter, white matter, and cerebrospinal fluid) with totally 12 time-points from birth to 4 years of age (i.e., 1, 2, 3, 4, 5, 6, 9, 12, 18, 24, 36, and 48 months of age) based on 175 longitudinal structural MRI scans from 39 typically-developing cynomolgus macaques, by leveraging state-of-the-art computational techniques tailored for early developing brains. Furthermore, to facilitate region-based analysis using our atlases, we also provide two popular hierarchy parcellations, i.e., cortical hierarchy maps (6 levels) and subcortical hierarchy maps (6 levels), on our longitudinal macaque brain atlases. These early developing atlases, which have the densest time-points during infancy (to the best of our knowledge), will greatly facilitate the studies of macaque brain development.

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YNIMG Journal 2020 Journal Article

(TS)2WM: Tumor Segmentation and Tract Statistics for Assessing White Matter Integrity with Applications to Glioblastoma Patients

Liming Zhong
Tengfei Li
Hai Shu
Chao Huang
Jason Michael Johnson
Donald F Schomer
Ho-Ling Liu
Qianjin Feng

N model achieves competitive tumor segmentation accuracy compared with many state-of-the-art tumor segmentation methods. Significant differences in various DTI-related measurements at the streamline, weighted network, and binary network levels (e.g., diffusion properties along major fiber bundles) were found in tumor-related, language-related, and hand motor-related brain regions in 62 GBM patients as compared to healthy subjects from the Human Connectome Project.

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AAAI Conference 2019 Conference Paper

A Powerful Global Test Statistic for Functional Statistical Inference

Jingwen Zhang
Joseph Ibrahim
Tengfei Li
Hongtu Zhu

We consider the problem of performing an association test between functional data and scalar variables in a varying coefficient model setting. We propose a functional projection regression model and an associated global test statistic to aggregate relatively weak signals across the domain of functional data, while reducing the dimension. An optimal functional projection direction is selected to maximize signal-to-noise ratio with ridge penalty. Theoretically, we systematically study the asymptotic distribution of the global test statistic and provide a strategy to adaptively select the optimal tuning parameter. We use simulations to show that the proposed test outperforms all existing state-of-the-art methods in functional statistical inference. Finally, we apply the proposed testing method to the genome-wide association analysis of imaging genetic data in UK Biobank dataset.

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YNIMG Journal 2019 Journal Article

Brain functional development separates into three distinct time periods in the first two years of life

Weiyan Yin
Meng-Hsiang Chen
Sheng-Che Hung
Kristine R. Baluyot
Tengfei Li
Weili Lin

Recently, resting functional MRI has provided invaluable insight into the brain developmental processes of early infancy and childhood. A common feature of previous functional development studies is the use of age to separate subjects into different cohorts for group comparisons. However, functional maturation paces vary tremendously from subject to subject. Since this is particularly true for the first years of life, an alternative to physical age alone is needed for cluster analysis. Here, a data-driven approach based on individual brain functional connectivity was employed to cluster typically developing children who were longitudinally imaged using MRI without sedation for the first two years of life. Specifically, three time periods were determined based on the distinction of brain functional connectivity patterns, including 0–1 month (group 1), 2–7 months (group 2), and 8–24 (group 3) of age, respectively. From groups 1 to 2, connection density increased by almost two-fold, local efficacy (LE) is significantly improved, and there was no change in global efficiency (GE). From groups 2 to 3, connection density increased slightly, LE showed no change, and a significant increase in GE were observed. Furthermore, 27 core brain regions were identified which yielded clustering results that resemble those obtained using all brain regions. These core regions were largely associated with the motor, visual and language functional domains as well as regions associated with higher order cognitive functional domains. Both visual and language functional domains exhibited a persistent and significant increase within domain connection from groups 1 to 3, while no changes were observed for the motor domain. In contrast, while a reduction of inter-domain connection was the general developmental pattern, the motor domain exhibited an interesting “V” shape pattern in its relationship to visual and language associated areas, showing a decrease from groups 1 to 2, followed by an increase from groups 2 to 3. In summary, our results offer new insights into functional brain development and identify 27 core brain regions critically important for early brain development.

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NeurIPS Conference 2019 Conference Paper

Graph-Based Semi-Supervised Learning with Non-ignorable Non-response

Fan Zhou
Tengfei Li
Haibo Zhou
Hongtu Zhu
Ye Jieping

Graph-based semi-supervised learning is a very powerful tool in classification tasks, while in most existing literature the labelled nodes are assumed to be randomly sampled. When the labelling status depends on the unobserved node response, ignoring the missingness can lead to significant estimation bias and handicap the classifiers. This situation is called non-ignorable non-response. To solve the problem, we propose a Graph-based joint model with Non-ignorable Non-response (GNN), followed by a joint inverse weighting estimation procedure incorporated with sampling imputation approach. Our method is proved to outperform some state-of-art models in both regression and classification problems, by simulations and real analysis on the Cora dataset.

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