Search Results for author: Alejandro F. Frangi

Found 28 papers, 6 papers with code

Unsupervised Domain Adaptation for Brain Vessel Segmentation through Transwarp Contrastive Learning

1 code implementation23 Feb 2024 Fengming Lin, Yan Xia, Michael MacRaild, Yash Deo, Haoran Dou, Qiongyao Liu, Kun Wu, Nishant Ravikumar, Alejandro F. Frangi

Unsupervised domain adaptation (UDA) aims to align the labelled source distribution with the unlabelled target distribution to obtain domain-invariant predictive models.

Learned Local Attention Maps for Synthesising Vessel Segmentations

no code implementations24 Aug 2023 Yash Deo, Rodrigo Bonazzola, Haoran Dou, Yan Xia, Tianyou Wei, Nishant Ravikumar, Alejandro F. Frangi, Toni Lassila

We present an encoder-decoder model for synthesising segmentations of the main cerebral arteries in the circle of Willis (CoW) from only T2 MRI.

Shape-guided Conditional Latent Diffusion Models for Synthesising Brain Vasculature

no code implementations13 Aug 2023 Yash Deo, Haoran Dou, Nishant Ravikumar, Alejandro F. Frangi, Toni Lassila

The Circle of Willis (CoW) is the part of cerebral vasculature responsible for delivering blood to the brain.

GSMorph: Gradient Surgery for cine-MRI Cardiac Deformable Registration

1 code implementation26 Jun 2023 Haoran Dou, Ning Bi, Luyi Han, Yuhao Huang, Ritse Mann, Xin Yang, Dong Ni, Nishant Ravikumar, Alejandro F. Frangi, Yunzhi Huang

In this study, we construct a registration model based on the gradient surgery mechanism, named GSMorph, to achieve a hyperparameter-free balance on multiple losses.

A Generalised Deep Meta-Learning Model for Automated Quality Control of Cardiovascular Magnetic Resonance Images

1 code implementation23 Mar 2023 Shahabedin Nabavi, Hossein Simchi, Mohsen Ebrahimi Moghaddam, Ahmad Ali Abin, Alejandro F. Frangi

Methods: The proposed generalised deep meta-learning model can evaluate the quality by learning tasks in the prior stage and then fine-tuning the resulting model on a small labelled dataset of the desired tasks.

Domain Adaptation Image Quality Assessment +1

Boosting Personalised Musculoskeletal Modelling with Physics-informed Knowledge Transfer

no code implementations22 Nov 2022 Jie Zhang, Yihui Zhao, Tianzhe Bao, Zhenhong Li, Kun Qian, Alejandro F. Frangi, Sheng Quan Xie, Zhi-Qiang Zhang

The salient advantages of the proposed framework are twofold: 1) For the generic model, physics-based domain knowledge is embedded into the loss function of the data-driven model as soft constraints to penalise/regularise the data-driven model.

Transfer Learning

A Generative Shape Compositional Framework: Towards Representative Populations of Virtual Heart Chimaeras

no code implementations4 Oct 2022 Haoran Dou, Seppo Virtanen, Nishant Ravikumar, Alejandro F. Frangi

Specifically, we propose a generative shape compositional framework which comprises two components - a part-aware generative shape model which captures the variability in shape observed for each structure of interest in the training population; and a spatial composition network which assembles/composes the structures synthesised by the former into multi-part shape assemblies (viz.

Anatomy Self-Supervised Learning +1

Three-dimensional micro-structurally informed in silico myocardium -- towards virtual imaging trials in cardiac diffusion weighted MRI

no code implementations22 Aug 2022 Mojtaba Lashgari, Nishant Ravikumar, Irvin Teh, Jing-Rebecca Li, David L. Buckley, Jurgen E. Schneider, Alejandro F. Frangi

We extend previous studies accounting for the cardiomyocyte shape variability, water exchange between the cardiomyocytes (intercalated discs), myocardial microstructure disarray, and four sheetlet orientations.

Localizing the Recurrent Laryngeal Nerve via Ultrasound with a Bayesian Shape Framework

1 code implementation30 Jun 2022 Haoran Dou, Luyi Han, Yushuang He, Jun Xu, Nishant Ravikumar, Ritse Mann, Alejandro F. Frangi, Pew-Thian Yap, Yunzhi Huang

Tumor infiltration of the recurrent laryngeal nerve (RLN) is a contraindication for robotic thyroidectomy and can be difficult to detect via standard laryngoscopy.

Fully Automated Assessment of Cardiac Coverage in Cine Cardiovascular Magnetic Resonance Images using an Explainable Deep Visual Salient Region Detection Model

no code implementations14 Jun 2022 Shahabedin Nabavi, Mohammad Hashemi, Mohsen Ebrahimi Moghaddam, Ahmad Ali Abin, Alejandro F. Frangi

The accuracy of the baseline model in identifying the presence/absence of basal/apical slices is 96. 25\% and 94. 51\%, respectively, which increases to 96. 88\% and 95. 72\% after improving using the proposed salient region detection model.

AI-based Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo

no code implementations6 Aug 2021 Yuan Jin, Antonio Pepe, Jianning Li, Christina Gsaxner, Fen-hua Zhao, Kelsey L. Pomykala, Jens Kleesiek, Alejandro F. Frangi, Jan Egger

The standard imaging modality for diagnosis and monitoring is computed tomography (CT), which can provide a detailed picture of the aorta and its branching vessels if completed with a contrast agent, called CT angiography (CTA).

Computed Tomography (CT)

Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma Segmentation

1 code implementation7 Jul 2021 Mohammad Hamghalam, Alejandro F. Frangi, Baiying Lei, Amber L. Simpson

In large studies involving multi protocol Magnetic Resonance Imaging (MRI), it can occur to miss one or more sub-modalities for a given patient owing to poor quality (e. g. imaging artifacts), failed acquisitions, or hallway interrupted imaging examinations.

Brain Tumor Segmentation Segmentation +1

Medical Imaging and Computational Image Analysis in COVID-19 Diagnosis: A Review

no code implementations1 Oct 2020 Shahabedin Nabavi, Azar Ejmalian, Mohsen Ebrahimi Moghaddam, Ahmad Ali Abin, Alejandro F. Frangi, Mohammad Mohammadi, Hamidreza Saligheh Rad

The contribution of this study is four-fold: 1) to use as a tutorial of the field for both clinicians and technologists, 2) to comprehensively review the characteristics of COVID-19 as presented in medical images, 3) to examine automated artificial intelligence-based approaches for COVID-19 diagnosis based on the accuracy and the method used, 4) to express the research limitations in this field and the methods used to overcome them.

COVID-19 Diagnosis

Fed-Sim: Federated Simulation for Medical Imaging

no code implementations1 Sep 2020 Daiqing Li, Amlan Kar, Nishant Ravikumar, Alejandro F. Frangi, Sanja Fidler

Since the model of geometry and material is disentangled from the imaging sensor, it can effectively be trained across multiple medical centers.

Federated Learning

Partially Conditioned Generative Adversarial Networks

no code implementations6 Jul 2020 Francisco J. Ibarrola, Nishant Ravikumar, Alejandro F. Frangi

With the introduction of Conditional GANs and their variants, these methods were extended to generating samples conditioned on ancillary information available for each sample within the dataset.

Image Generation

Automatic Assessment of Full Left Ventricular Coverage in Cardiac Cine Magnetic Resonance Imaging with Fisher-Discriminative 3D CNN

no code implementations6 Nov 2018 Le Zhang, Ali Gooya, Marco Pereanez, Bo Dong, Stefan K. Piechnik, Stefan Neubauer, Steffen E. Petersen, Alejandro F. Frangi

Full coverage of the left ventricle (LV), from base to apex, is a basic criterion for CMR image quality and necessary for accurate measurement of cardiac volume and functional assessment.

Essence of kernel Fisher discriminant: KPCA plus LDA

no code implementations Pattern Recognition 2003 Jian Yang; Zhong Jin; Jing-yu Yang, David Zhang, Alejandro F. Frangi

In this paper, the method of kernel Fisher discriminant (KFD) is analyzed and its nature is revealed, i. e., KFD is equivalent to kernel principal component analysis (KPCA) plus Fisher linear discriminant analysis (LDA).

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