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Self-Consistent Nested Diffusion Bridge for Accelerated MRI Reconstruction
Tao Song, Yicheng Wu, Minhao Hu +6
Accelerated MRI reconstruction plays a vital role in reducing scan time while preserving image quality. While most existing methods rely on complex-valued image-space or k-space da…
Learning Modality-Aware Representations: Adaptive Group-wise Interaction Network for Multimodal MRI Synthesis
Tao Song, Yicheng Wu, Minhao Hu +6
Multimodal MR image synthesis aims to generate missing modality images by effectively fusing and mapping from a subset of available MRI modalities. Most existing methods adopt an i…
Rethinking Abdominal Organ Segmentation (RAOS) in the clinical scenario: A robustness evaluation benchmark with challenging cases
Xiangde Luo, Zihan Li, Shaoting Zhang +2
Deep learning has enabled great strides in abdominal multi-organ segmentation, even surpassing junior oncologists on common cases or organs. However, robustness on corner cases and…
Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center Dataset
Hongqiu Wang, Xiangde Luo, Wu Chen +4
Accurate vessel segmentation in Ultra-Wide-Field Scanning Laser Ophthalmoscopy (UWF-SLO) images is crucial for diagnosing retinal diseases. Although recent techniques have shown en…
SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
Xiangde Luo, Jia Fu, Yunxin Zhong +39
Radiation therapy is a primary and effective NasoPharyngeal Carcinoma (NPC) treatment strategy. The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is c…
PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation
Guotai Wang, Xiangde Luo, Ran Gu +6
Background and Objective: Open-source deep learning toolkits are one of the driving forces for developing medical image segmentation models. Existing toolkits mainly focus on fully…
Scribble-Supervised Medical Image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervision
Xiangde Luo, Minhao Hu, Wenjun Liao +4
Medical image segmentation plays an irreplaceable role in computer-assisted diagnosis, treatment planning, and following-up. Collecting and annotating a large-scale dataset is cruc…
Plug-and-play Shape Refinement Framework for Multi-site and Lifespan Brain Skull Stripping
Yunxiang Li, Ruilong Dan, Shuai Wang +8
Skull stripping is a crucial prerequisite step in the analysis of brain magnetic resonance images (MRI). Although many excellent works or tools have been proposed, they suffer from…
Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformer
Xiangde Luo, Minhao Hu, Tao Song +2
Recently, deep learning with Convolutional Neural Networks (CNNs) and Transformers has shown encouraging results in fully supervised medical image segmentation. However, it is stil…
Medical Image Segmentation Using Squeeze-and-Expansion Transformers
Shaohua Li, Xiuchao Sui, Xiangde Luo +3
Medical image segmentation is important for computer-aided diagnosis. Good segmentation demands the model to see the big picture and fine details simultaneously, i.e., to learn ima…
Learning Euler's Elastica Model for Medical Image Segmentation
Xu Chen, Xiangde Luo, Yitian Zhao +3
Image segmentation is a fundamental topic in image processing and has been studied for many decades. Deep learning-based supervised segmentation models have achieved state-of-the-a…