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Pengcheng Lei

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3 papers

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JBHI Journal 2025 Journal Article

Deep Unfolding Segmentation Network for Under-sampled Magnetic Resonance Images

Le Hu
Pengcheng Lei
Faming Fang
Jie Dong
Guixu Zhang

Magnetic Resonance (MR) image segmentation is a critical task in assisting disease diagnosis. Most existing methods assume that the images being segmented are fully-sampled. However, they ignore the fact that MR images obtained in clinics are often reconstructed from under-sampled k-space data. There are artifacts or distorted details in the reconstruction, leading to unsatisfactory segmentation performance. In this paper, we propose an end-to-end deep unfolding framework to segment desired lesions or organs from the under-sampled k-space data. Specifically, we build a new model to combine the compressive sensing-based under-sampled image reconstruction and level-set-based segmentation. In this model, we introduce an L0 norm on the reconstruction images to enforce smoothing while preserving important edge and boundary, boosting downstream segmentation performance. We employ the Augmented Lagrangian Method to seek the solution and unfold the iterative algorithm into a deep neural network, called deep unfolding segmentation network (DUSNet). To further enhance segmentation performance, we introduce a boundary loss function, which encourages the model to effectively capture edge details of the regions of interest and imposes geometric constraints on the segmentation results. Through end-to-end training, DUSNet can efficiently segment target regions from under-sampled k-space data. Comprehensive experiments demonstrate that the proposed DUSNet outperforms existing state-of-the-art methods for under-sampled MR image segmentation, achieving superior segmentation accuracy.

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NeurIPS Conference 2025 Conference Paper

Surface-Aware Feed-Forward Quadratic Gaussian for Frame Interpolation with Large Motion

Zaoming Yan
Yaomin Huang
Pengcheng Lei
Qizhou Chen
Guixu Zhang
Faming Fang

Motion in the real world takes place in 3D space. Existing Frame Interpolation methods often estimate global receptive fields in 2D frame space. Due to the limitations of 2D space, these global receptive fields are limited, which makes it difficult to match object correspondences between frames, resulting in sub-optimal performance when handling large-motion scenarios. In this paper, we introduce a novel pipeline for exploring object correspondences based on differential surface theory. The differential surface coordinate system provides a better representation of the real world, enabling effective exploration of object correspondences. Specifically, the pipeline first transforms an input pair of video frames from the image coordinate system to the differential surface coordinate system. Subsequently, within this coordinate system, object correspondences are explored based on surface geometric properties and the surface uniqueness theorem. Experimental findings showcase that our method attains state-of-the-art performance across large motion benchmarks. Our method demonstrates the state-of-the-art performance on these VFI subsets with large motion.

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IJCAI Conference 2023 Conference Paper

Deep Unfolding Convolutional Dictionary Model for Multi-Contrast MRI Super-resolution and Reconstruction

Pengcheng Lei
Faming Fang
Guixu Zhang
Ming Xu

Magnetic resonance imaging (MRI) tasks often involve multiple contrasts. Recently, numerous deep learning-based multi-contrast MRI super-resolution (SR) and reconstruction methods have been proposed to explore the complementary information from the multi-contrast images. However, these methods either construct parameter-sharing networks or manually design fusion rules, failing to accurately model the correlations between multi-contrast images and lacking certain interpretations. In this paper, we propose a multi-contrast convolutional dictionary (MC-CDic) model under the guidance of the optimization algorithm with a well-designed data fidelity term. Specifically, we bulid an observation model for the multi-contrast MR images to explicitly model the multi-contrast images as common features and unique features. In this way, only the useful information in the reference image can be transferred to the target image, while the inconsistent information will be ignored. We employ the proximal gradient algorithm to optimize the model and unroll the iterative steps into a deep CDic model. Especially, the proximal operators are replaced by learnable ResNet. In addition, multi-scale dictionaries are introduced to further improve the model performance. We test our MC-CDic model on multi-contrast MRI SR and reconstruction tasks. Experimental results demonstrate the superior performance of the proposed MC-CDic model against existing SOTA methods. Code is available at https: //github. com/lpcccc-cv/MC-CDic.

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