Search Results for author: Tianfu Wang

Found 12 papers, 5 papers with code

Optimization-Based Eye Tracking using Deflectometric Information

no code implementations9 Mar 2023 Tianfu Wang, Jiazhang Wang, Oliver Cossairt, Florian Willomitzer

Eye tracking is an important tool with a wide range of applications in Virtual, Augmented, and Mixed Reality (VR/AR/MR) technologies.

Inverse Rendering Mixed Reality

Bootstrap Representation Learning for Segmentation on Medical Volumes and Sequences

no code implementations23 Jun 2021 Zejian Chen, Wei Zhuo, Tianfu Wang, Wufeng Xue, Dong Ni

Based on the continuity between slices/frames and the common spatial layout of organs across volumes/sequences, we introduced a novel bootstrap self-supervised representation learning method by leveraging the predictable possibility of neighboring slices.

Representation Learning Self-Supervised Learning

A New Weighting Scheme for Fan-beam and Circle Cone-beam CT Reconstructions

no code implementations6 Jan 2021 Wei Wang, Xiang-Gen Xia, Chuanjiang He, Zemin Ren, Jian Lu, Tianfu Wang, Baiying Lei

In this paper, we first present an arc based algorithm for fan-beam computed tomography (CT) reconstruction via applying Katsevich's helical CT formula to 2D fan-beam CT reconstruction.

Computed Tomography (CT) SSIM

Convolutional 3D to 2D Patch Conversion for Pixel-wise Glioma Segmentation in MRI Scans

no code implementations20 Oct 2020 Mohammad Hamghalam, Baiying Lei, Tianfu Wang

Structural magnetic resonance imaging (MRI) has been widely utilized for analysis and diagnosis of brain diseases.

A model-guided deep network for limited-angle computed tomography

1 code implementation10 Aug 2020 Wei Wang, Xiang-Gen Xia, Chuanjiang He, Zemin Ren, Jian Lu, Tianfu Wang, Baiying Lei

In this paper, we first propose a variational model for the limited-angle computed tomography (CT) image reconstruction and then convert the model into an end-to-end deep network. We use the penalty method to solve the model and divide it into three iterative subproblems, where the first subproblem completes the sinograms by utilizing the prior information of sinograms in the frequency domain and the second refines the CT images by using the prior information of CT images in the spatial domain, and the last merges the outputs of the first two subproblems.

Computed Tomography (CT) Image Reconstruction

High Tissue Contrast MRI Synthesis Using Multi-Stage Attention-GAN for Glioma Segmentation

no code implementations9 Jun 2020 Mohammad Hamghalam, Baiying Lei, Tianfu Wang

We also employ HTC MR images in both the end-to-end and two-stage segmentation structure to confirm the effectiveness of these images.

Image-to-Image Translation

A deep network for sinogram and CT image reconstruction

1 code implementation20 Jan 2020 Wei Wang, Xiang-Gen Xia, Chuanjiang He, Zemin Ren, Jian Lu, Tianfu Wang, Baiying Lei

A CT image can be well reconstructed when the sampling rate of the sinogram satisfies the Nyquist criteria and the sampled signal is noise-free.

Denoising Image Reconstruction +1

SANet:Superpixel Attention Network for Skin Lesion Attributes Detection

no code implementations20 Oct 2019 Xinzi He, Baiying Lei, Tianfu Wang

We introduce a superpixel average pooling to reformulate the superpixel classification problem as a superpixel segmentation problem and a SAMis utilized to focus on discriminative superpixel regions and feature channels.

General Classification Lesion Segmentation

Brain Tumor Synthetic Segmentation in 3D Multimodal MRI Scans

no code implementations27 Sep 2019 Mohammad Hamghalam, Baiying Lei, Tianfu Wang

A comparison of these synthetic images and real images of brain tumor tissue in MR scans showed significant segmentation improvement and decreased the number of real channels for segmentation.

Brain Tumor Segmentation Tumor Segmentation

Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound

1 code implementation3 Jul 2019 Yi Wang, Haoran Dou, Xiao-Wei Hu, Lei Zhu, Xin Yang, Ming Xu, Jing Qin, Pheng-Ann Heng, Tianfu Wang, Dong Ni

Our attention module utilizes the attention mechanism to selectively leverage the multilevel features integrated from different layers to refine the features at each individual layer, suppressing the non-prostate noise at shallow layers of the CNN and increasing more prostate details into features at deep layers.

Image Segmentation Medical Image Segmentation +1

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