175 citations · 212 across the 33 of their papers we have counts for
35 papers
MTCNet: Motion and Topology Consistency Guided Learning for Mitral Valve Segmentationin 4D Ultrasound
Rusi Chen, Yuanting Yang, Jiezhi Yao +12
Mitral regurgitation is one of the most prevalent cardiac disorders. Four-dimensional (4D) ultrasound has emerged as the primary imaging modality for assessing dynamic valvular mor…
UltraTwin: Towards Cardiac Anatomical Twin Generation from Multi-view 2D Ultrasound
Junxuan Yu, Yaofei Duan, Yuhao Huang +22
Echocardiography is routine for cardiac examination. However, 2D ultrasound (US) struggles with accurate metric calculation and direct observation of 3D cardiac structures. Moreove…
Uncertainty-aware Diffusion and Reinforcement Learning for Joint Plane Localization and Anomaly Diagnosis in 3D Ultrasound
Yuhao Huang, Yueyue Xu, Haoran Dou +4
Congenital uterine anomalies (CUAs) can lead to infertility, miscarriage, preterm birth, and an increased risk of pregnancy complications. Compared to traditional 2D ultrasound (US…
From Pixels to Polygons: A Survey of Deep Learning Approaches for Medical Image-to-Mesh Reconstruction
Fengming Lin, Arezoo Zakeri, Yidan Xue +7
Deep learning-based medical image-to-mesh reconstruction has rapidly evolved, enabling the transformation of medical imaging data into three-dimensional mesh models that are critic…
Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasound
Yuhao Huang, Ao Chang, Haoran Dou +8
Accurate segmentation of nodules in both 2D breast ultrasound (BUS) and 3D automated breast ultrasound (ABUS) is crucial for clinical diagnosis and treatment planning. Therefore, d…
Ctrl-GenAug: Controllable Generative Augmentation for Medical Sequence Classification
Xinrui Zhou, Yuhao Huang, Haoran Dou +13
In the medical field, the limited availability of large-scale datasets and labor-intensive annotation processes hinder the performance of deep models. Diffusion-based generative au…
Robust Box Prompt based SAM for Medical Image Segmentation
Yuhao Huang, Xin Yang, Han Zhou +4
The Segment Anything Model (SAM) can achieve satisfactory segmentation performance under high-quality box prompts. However, SAM's robustness is compromised by the decline in box qu…
Non-Adversarial Learning: Vector-Quantized Common Latent Space for Multi-Sequence MRI
Luyi Han, Tao Tan, Tianyu Zhang +7
Adversarial learning helps generative models translate MRI from source to target sequence when lacking paired samples. However, implementing MRI synthesis with adversarial learning…
GS-EMA: Integrating Gradient Surgery Exponential Moving Average with Boundary-Aware Contrastive Learning for Enhanced Domain Generalization in Aneurysm Segmentation
Fengming Lin, Yan Xia, Michael MacRaild +6
The automated segmentation of cerebral aneurysms is pivotal for accurate diagnosis and treatment planning. Confronted with significant domain shifts and class imbalance in 3D Rotat…
Unsupervised Domain Adaptation for Brain Vessel Segmentation through Transwarp Contrastive Learning
Fengming Lin, Yan Xia, Michael MacRaild +6
Unsupervised domain adaptation (UDA) aims to align the labelled source distribution with the unlabelled target distribution to obtain domain-invariant predictive models. Since cros…
Learned Local Attention Maps for Synthesising Vessel Segmentations
Yash Deo, Rodrigo Bonazzola, Haoran Dou +5
Magnetic resonance angiography (MRA) is an imaging modality for visualising blood vessels. It is useful for several diagnostic applications and for assessing the risk of adverse ev…
Shape-guided Conditional Latent Diffusion Models for Synthesising Brain Vasculature
Yash Deo, Haoran Dou, Nishant Ravikumar +2
The Circle of Willis (CoW) is the part of cerebral vasculature responsible for delivering blood to the brain. Understanding the diverse anatomical variations and configurations of…
A Conditional Flow Variational Autoencoder for Controllable Synthesis of Virtual Populations of Anatomy
Haoran Dou, Nishant Ravikumar, Alejandro F. Frangi
The generation of virtual populations (VPs) of anatomy is essential for conducting in silico trials of medical devices. Typically, the generated VP should capture sufficient variab…
GSMorph: Gradient Surgery for cine-MRI Cardiac Deformable Registration
Haoran Dou, Ning Bi, Luyi Han +7
Deep learning-based deformable registration methods have been widely investigated in diverse medical applications. Learning-based deformable registration relies on weighted objecti…
An Explainable Deep Framework: Towards Task-Specific Fusion for Multi-to-One MRI Synthesis
Luyi Han, Tianyu Zhang, Yunzhi Huang +6
Multi-sequence MRI is valuable in clinical settings for reliable diagnosis and treatment prognosis, but some sequences may be unusable or missing for various reasons. To address th…
Fourier Test-time Adaptation with Multi-level Consistency for Robust Classification
Yuhao Huang, Xin Yang, Xiaoqiong Huang +7
Deep classifiers may encounter significant performance degradation when processing unseen testing data from varying centers, vendors, and protocols. Ensuring the robustness of deep…
A Generative Shape Compositional Framework to Synthesise Populations of Virtual Chimaeras
Haoran Dou, Seppo Virtanen, Nishant Ravikumar +1
Generating virtual populations of anatomy that capture sufficient variability while remaining plausible is essential for conducting in-silico trials of medical devices. However, no…
Online Reflective Learning for Robust Medical Image Segmentation
Yuhao Huang, Xin Yang, Xiaoqiong Huang +7
Deep segmentation models often face the failure risks when the testing image presents unseen distributions. Improving model robustness against these risks is crucial for the large-…
Agent with Tangent-based Formulation and Anatomical Perception for Standard Plane Localization in 3D Ultrasound
Yuxin Zou, Haoran Dou, Yuhao Huang +9
Standard plane (SP) localization is essential in routine clinical ultrasound (US) diagnosis. Compared to 2D US, 3D US can acquire multiple view planes in one scan and provide compl…
Localizing the Recurrent Laryngeal Nerve via Ultrasound with a Bayesian Shape Framework
Haoran Dou, Luyi Han, Yushuang He +6
Tumor infiltration of the recurrent laryngeal nerve (RLN) is a contraindication for robotic thyroidectomy and can be difficult to detect via standard laryngoscopy. Ultrasound (US)…
Deformable Registration of Brain MR Images via a Hybrid Loss
Luyi Han, Haoran Dou, Yunzhi Huang +1
Unsupervised learning strategy is widely adopted by the deformable registration models due to the lack of ground truth of deformation fields. These models typically depend on the i…
Flip Learning: Erase to Segment
Yuhao Huang, Xin Yang, Yuxin Zou +7
Nodule segmentation from breast ultrasound images is challenging yet essential for the diagnosis. Weakly-supervised segmentation (WSS) can help reduce time-consuming and cumbersome…
Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound
Xin Yang, Yuhao Huang, Ruobing Huang +10
3D ultrasound (US) has become prevalent due to its rich spatial and diagnostic information not contained in 2D US. Moreover, 3D US can contain multiple standard planes (SPs) in one…
Agent with Warm Start and Adaptive Dynamic Termination for Plane Localization in 3D Ultrasound
Xin Yang, Haoran Dou, Ruobing Huang +9
Accurate standard plane (SP) localization is the fundamental step for prenatal ultrasound (US) diagnosis. Typically, dozens of US SPs are collected to determine the clinical diagno…