26 citations · 56 across the 26 of their papers we have counts for
11 papers · 1 filter
Shape-aware Sampling Matters in the Modeling of Multi-Class Tubular Structures
Minghui Zhang, Yaoyu Liu, Xin You +2
Accurate multi-class tubular modeling is critical for precise lesion localization and optimal treatment planning. Deep learning methods enable automated shape modeling by prioritiz…
Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge
Muhammad Imran, Jonathan R. Krebs, Vishal Balaji Sivaraman +60
Multi-class segmentation of the aorta in computed tomography angiography (CTA) scans is essential for diagnosing and planning complex endovascular treatments for patients with aort…
AirMorph: Topology-Preserving Deep Learning for Pulmonary Airway Analysis
Minghui Zhang, Chenyu Li, Fangfang Xie +8
Accurate anatomical labeling and analysis of the pulmonary structure and its surrounding anatomy from thoracic CT is getting increasingly important for understanding the etilogy of…
Topology-Aware Exploration of Circle of Willis for CTA and MRA: Segmentation, Detection, and Classification
Minghui Zhang, Xin You, Hanxiao Zhang +1
The Circle of Willis (CoW) vessels is critical to connecting major circulations of the brain. The topology of the vascular structure is clinical significance to evaluate the risk,…
Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge
Yang Nan, Xiaodan Xing, Shiyi Wang +38
Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway trees remains…
Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation
Minghui Zhang, Yangqian Wu, Hanxiao Zhang +33
Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach o…
Re-thinking and Re-labeling LIDC-IDRI for Robust Pulmonary Cancer Prediction
Hanxiao Zhang, Xiao Gu, Minghui Zhang +6
The LIDC-IDRI database is the most popular benchmark for lung cancer prediction. However, with subjective assessment from radiologists, nodules in LIDC may have entirely different…
Faithful learning with sure data for lung nodule diagnosis
Hanxiao Zhang, Liang Chen, Xiao Gu +6
Recent evolution in deep learning has proven its value for CT-based lung nodule classification. Most current techniques are intrinsically black-box systems, suffering from two gene…
BREAK: Bronchi Reconstruction by gEodesic transformation And sKeleton embedding
Weihao Yu, Hao Zheng, Minghui Zhang +3
Airway segmentation is critical for virtual bronchoscopy and computer-aided pulmonary disease analysis. In recent years, convolutional neural networks (CNNs) have been widely used…
FDA: Feature Decomposition and Aggregation for Robust Airway Segmentation
Minghui Zhang, Xin Yu, Hanxiao Zhang +5
3D Convolutional Neural Networks (CNNs) have been widely adopted for airway segmentation. The performance of 3D CNNs is greatly influenced by the dataset while the public airway da…
DDU-Nets: Distributed Dense Model for 3D MRI Brain Tumor Segmentation
Hanxiao Zhang, Jingxiong Li, Mali Shen +2
Segmentation of brain tumors and their subregions remains a challenging task due to their weak features and deformable shapes. In this paper, three patterns (cross-skip, skip-1 and…