Pseudo-healthy Image Synthesis for White Matter Lesion Segmentation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9968)


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-specific pathology-free synthetic FLAIR image from a T\(_1\) -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 identification of VD in a dataset of 468 dementia patients. In this application the automatically calculated features perform comparably to clinically derived Fazekas scores.


Weighted Image Vascular Dementia White Matter Hyperintensities Synthetic Image Flair Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of ComputingImperial College LondonLondonUK
  2. 2.Imanova Ltd.LondonUK
  3. 3.PET Centre, Kings College LondonLondonUK
  4. 4.Department of Neuroimaging SciencesUniversity of EdinburghEdinburghUK
  5. 5.Alzheimer Center and Department of NeurologyVU University Medical Center, Amsterdam NeuroscienceAmsterdamThe Netherlands
  6. 6.Department of Radiology and Nuclear MedicineVU University Medical Center, Amsterdam NeuroscienceAmsterdamThe Netherlands
  7. 7.Department of MedicineImperial College LondonLondonUK
  8. 8.IXICO Technologies Ltd.LondonUK

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