Pseudo-Healthy Image Synthesis for White Matter Lesion Segmentation

Christopher Bowles, Chen Qin, Christian Ledig, Ricardo Guerrero Moreno, Roger Gunn, Alexander Hammers, Eleni Sakka, David Alexander Dickie, Maria Valdes Hernandez, Natalie Royle, Joanna Wardlaw, Hanneke Rhodius-Meester, Betty Tijms, Afina Lemstra, Wiesje van der Flier, Frederik Barkhof, Philip Scheltens, Daniel Rueckert

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

White matter hyperintensities (WMH) seen on FLAIR images are established as a key indicator of Vascular Dementia (VD) and other pathologies.We propose a novel modality transformation technique to generate a subject-specifc pathology-free synthetic FLAIR image from a T1 -weighted image. WMH are then accurately segmented by comparing this synthesized FLAIR image to the actually acquired FLAIR image. We term this method Pseudo-Healthy Image Synthesis (PHI-Syn). The method is evaluated on data from 42 stroke patients where we compare its performance to two commonly used methods from the Lesion Segmentation Toolbox. We show that the proposed method achieves superior performance for a number of metrics. Finally, we show that the features extracted from the WMH segmentations can be used to predict a Fazekas lesion score that supports the identifcation of VD in a dataset of 468 dementia patients. In this application the automatically calculated features perform comparably to clinically derived Fazekas scores.
Original languageEnglish
Title of host publicationSimulation and Synthesis in Medical Imaging
ISBN (Electronic)978-3-319-46630-9
ISBN (Print)978-3-319-46629-3
Publication statusPublished - 23 Sept 2016

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Link


Dive into the research topics of 'Pseudo-Healthy Image Synthesis for White Matter Lesion Segmentation'. Together they form a unique fingerprint.

Cite this