H
Huaqi Qiu
Researcher at Imperial College London
Publications - 23
Citations - 791
Huaqi Qiu is an academic researcher from Imperial College London. The author has contributed to research in topics: Image segmentation & Computer science. The author has an hindex of 5, co-authored 18 publications receiving 308 citations.
Papers
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Journal ArticleDOI
Deep learning for cardiac image segmentation: A review
Chen Chen,Chen Qin,Huaqi Qiu,Giacomo Tarroni,Giacomo Tarroni,Jinming Duan,Wenjia Bai,Daniel Rueckert +7 more
TL;DR: In this article, a review of deep learning-based segmentation methods for cardiac image segmentation is provided, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound.
Journal ArticleDOI
Deep Learning for Cardiac Image Segmentation: A Review.
Chen Chen,Chen Qin,Huaqi Qiu,Giacomo Tarroni,Giacomo Tarroni,Jinming Duan,Wenjia Bai,Daniel Rueckert +7 more
TL;DR: In this article, a review of deep learning-based segmentation methods for cardiac image segmentation is provided, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound and major anatomical structures of interest (ventricles, atria and vessels).
Book ChapterDOI
Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation
TL;DR: A novel self-supervised FSS framework for medical images in order to eliminate the requirement for annotations during training, and superpixel-based pseudo-labels are generated to provide supervision.
Book ChapterDOI
Unsupervised Multi-modal Style Transfer for Cardiac MR Segmentation
TL;DR: This work presents a fully automatic method to segment cardiac structures from late-gadolinium enhanced (LGE) images without using labelled LGE data for training, but instead by transferring the anatomical knowledge and features learned on annotated balanced steady-state free precession (bSSFP) images, which are easier to acquire.
Book ChapterDOI
Realistic Adversarial Data Augmentation for MR Image Segmentation
Chen Chen,Chen Qin,Huaqi Qiu,Cheng Ouyang,Shuo Wang,Liang Chen,Giacomo Tarroni,Wenjia Bai,Daniel Rueckert +8 more
TL;DR: This work proposes an adversarial data augmentation method for training neural networks for medical image segmentation, and shows that such an approach can improve the generalization ability and robustness of models as well as provide significant improvements in low-data scenarios.