Feature-space transformation improves supervised segmentation across scanners

Annegreet van Opbroek*, Hakim C. Achterberg, Marleen de Bruijne

*Corresponding author for this work

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review

4 Citations (Scopus)


Image-segmentation techniques based on supervised classification generally perform well on the condition that training and test samples have the same feature distribution. However, if training and test images are acquired with different scanners or scanning parameters, their feature distributions can be very different, which can hurt the performance of such techniques. We propose a feature-space-transformation method to overcome these differences in feature distributions. Our method learns a mapping of the feature values of training voxels to values observed in images from the test scanner. This transformation is learned from unlabeled images of subjects scanned on both the training scanner and the test scanner. We evaluated our method on hippocampus segmentation on 27 images of the Harmonized Hippocampal Protocol (HarP), a heterogeneous dataset consisting of 1.5T and 3T MR images. The results showed that our feature space transformation improved the Dice overlap of segmentations obtained with an SVM classifier from 0.36 to 0.85 when only 10 atlases were used and from 0.79 to 0.85 when around 100 atlases were used.

Original languageEnglish
Title of host publicationMachine Learning Meets Medical Imaging - 1st International Workshop, MLMMI 2015 Held in Conjunction with ICML 2015, Revised Selected Papers
EditorsKanwal K. Bhatia, Herve Lombaert
Place of PublicationCham
Number of pages9
ISBN (Electronic)9783319279299
ISBN (Print)9783319279282
Publication statusPublished - 2015
Event1st International Workshop on Machine Learning Meets Medical Imaging, MLMMI 2015 - Lille, France
Duration: 11 Jul 201511 Jul 2015

Publication series

SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)


Conference1st International Workshop on Machine Learning Meets Medical Imaging, MLMMI 2015

Bibliographical note

Funding Information: This research is financed by The Netherlands Organization for Scientific Research (NWO).

Publisher Copyright: © Springer International Publishing Switzerland 2015.


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