923 citations · 1.2k across the 30 of their papers we have counts for
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Intensity Confusion Matters: An Intensity-Distance Guided Loss for Bronchus Segmentation
Haifan Gong, Wenhao Huang, Huan Zhang +5
Automatic segmentation of the bronchial tree from CT imaging is important, as it provides structural information for disease diagnosis. Despite the merits of previous automatic bro…
ASC: Appearance and Structure Consistency for Unsupervised Domain Adaptation in Fetal Brain MRI Segmentation
Zihang Xu, Haifan Gong, Xiang Wan +1
Automatic tissue segmentation of fetal brain images is essential for the quantitative analysis of prenatal neurodevelopment. However, producing voxel-level annotations of fetal bra…
Diffusion-based Data Augmentation for Nuclei Image Segmentation
Xinyi Yu, Guanbin Li, Wei Lou +4
Nuclei segmentation is a fundamental but challenging task in the quantitative analysis of histopathology images. Although fully-supervised deep learning-based methods have made sig…
The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions
Jun Ma, Ronald Xie, Shamini Ayyadhury +37
Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or req…
BCNet: Bronchus Classification via Structure Guided Representation Learning
Wenhao Huang, Haifan Gong, Huan Zhang +5
CT-based bronchial tree analysis is essential for diagnosing lung and airway diseases, yet automatic bronchus classification remains challenging because bronchial topology varies s…