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Patch-based Segmentation with Spatial Consistency: Application to MS Lesions in Brain MRI


Affiliations
1 Biomedical Engineering Department, Tel-Aviv University, 69978 Tel Aviv, Israel
2 Bar-Ilan University, 52900 Ramat Gan, Israel
 

This paper presents an automatic lesion segmentation method based on similarities between multichannel patches. A patch database is built using training images for which the label maps are known. For each patch in the testing image, similar patches are retrieved from the database. The matching labels for these κ patches are then combined to produce an initial segmentation map for the test case. Finally an iterative patch-based label refinement process based on the initial segmentation map is performed to ensure the spatial consistency of the detected lesions. The method was evaluated in experiments on multiple sclerosis (MS) lesion segmentation in magnetic resonance images (MRI) of the brain. An evaluation was done for each image in the MICCAI 2008 MS lesion segmentation challenge. Results are shown to compete with the state of the art in the challenge. We conclude that the proposed algorithm for segmentation of lesions provides a promising new approach for local segmentation and global detection in medical images.
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  • Patch-based Segmentation with Spatial Consistency: Application to MS Lesions in Brain MRI

Abstract Views: 104  |  PDF Views: 2

Authors

Roey Mechrez
Biomedical Engineering Department, Tel-Aviv University, 69978 Tel Aviv, Israel
Jacob Goldberger
Bar-Ilan University, 52900 Ramat Gan, Israel
Hayit Greenspan
Biomedical Engineering Department, Tel-Aviv University, 69978 Tel Aviv, Israel

Abstract


This paper presents an automatic lesion segmentation method based on similarities between multichannel patches. A patch database is built using training images for which the label maps are known. For each patch in the testing image, similar patches are retrieved from the database. The matching labels for these κ patches are then combined to produce an initial segmentation map for the test case. Finally an iterative patch-based label refinement process based on the initial segmentation map is performed to ensure the spatial consistency of the detected lesions. The method was evaluated in experiments on multiple sclerosis (MS) lesion segmentation in magnetic resonance images (MRI) of the brain. An evaluation was done for each image in the MICCAI 2008 MS lesion segmentation challenge. Results are shown to compete with the state of the art in the challenge. We conclude that the proposed algorithm for segmentation of lesions provides a promising new approach for local segmentation and global detection in medical images.