Medical Image Segmentation:
The work is to perform image segmentation by using advanced machine learning techniques such as Dictionary Learning and Deep Learning.
Tong Tong, Robin Wolz, Zehan Wang, Qinquan Gao, Kazunari Misawa, Michitaka Fujiwara, Kensaku Mori, Joseph V. Hajnal, and Daniel Rueckert. “Discriminative Dictionary Learning for Abdominal Multi-Organ Segmentation”, Medical Image Analysis 2015
Tong Tong, Robin Wolz, Pierrick Coupé, Joseph V. Hajnal, Daniel Rueckert, and ADNI. “Segmentation of MR images via Discriminative Dictionary Learning and Sparse Coding: Application to Hippocampus Labeling”, NeuroImage 2013
Predict Alzheimer's Disease (Predict AD)
The aim of this work was to develop novel biomarkers for the diagnosis and prognosis of Alzheimer’s disease. To find out more please visit the PredictAD project.
Multiple Instance Learning for Classification of Alzheimer's Disease in Brain MRI
Tong Tong, Qinquan Gao, Ricardo Guerrero, Christian Ledig, Liang Chen, Daniel Rueckert. A Novel Grading Biomarker for the Prediction of Conversion from MCI to AD. IEEE Transaction on Biomedical Engineering, 2016.
Tong Tong, Robin Wolz, Qinquan Gao, Ricardo Guerrero, Joseph V. Hajnal, and Daniel Rueckert. “Multiple Instance Learning for Classification of Dementia in Brain MRI”, Medical Image Analysis 2014.
Predict Neurodegenerative Disease (Predict ND)
The aim of this work is to provide an accurate dignosis of different neurodegenerative diseases, including Alzhermer's diseases (AD), Frontotemporal dementia (FTD), dementia of Lewy body (DLB) and Vascular dementia (VaD). More details can be found in the PredictND Project.
Tong Tong, Christian Ledig, Ricardo Guerrero, Andreas Schuh, Juha Koikkalainen, Antti Tolonen, Hanneke Rhodius, Frederik Barkhof, Betty Tijms, Afina W Lemstra, Hilkka Soininen, Anne M Remes, Gunhild Waldemar, Steen Hasselbalch, Patrizia Mecocci, Marta Baroni, Jyrki Lötjönen, Wiesje van der Flier, Daniel Rueckert. Five-class Differential Diagnosis of Neurodegenerative Diseases using Random Undersampling Boosting, NeuroImage: Clinical 2017
Juha Koikkalainen, Hanneke Rhodius-Meester, Antti Tolonena, Frederik Barkhofc, Betty Tijms, Afina W. Lemstra, Tong Tong, Ricardo Guerrero, Andreas Schuh, Christian Ledig, Daniel Rueckert, Hilkka Soininen, Anne M. Remes, Gunhild Waldemar, Steen Hasselbalch, Patrizia Mecocci, Wiesje van der Flier, Jyrki Lötjönen. “Differential diagnosis of neurodegenerative diseases using structural MRI data” , NeuroImage: Clinical 2016
Functional Parcellation and Registration
The aim of this project is to improve the functional alignment using fMRI data and perform functional parcellation at both group and individual levels.
Tong Tong, Iman Aganj, Tian Ge, Jonathan R Polimeni, Bruce Fischl. Functional Density and Edge Maps: Characterizing Functional Architecture in Individuals and Improving Cross-subject Registration. NeuroImage, 2017
Laminar Segmentation and Modelling
In this project, we aim to develop tools for segmenting cortical areas and laminar boundaries using ultra-high resolution ex vivo MRI and optical coherence tomography (OCT), then to make in vivo inferences via probabilistic modeling.