Neil I Weisenfeld1, Simon K Warfield. 1. Computational Radiology Laboratory, Children's Hospital Boston, Harvard Medical School, Boston, MA, USA.
Abstract
PURPOSE: To develop an MRI segmentation method for brain tissues, regions, and substructures that yields improved classification accuracy. Current brain segmentation strategies include two complementary strategies. Multi-spectral classification techniques generate excellent segmentations for tissues with clear intensity contrast, but fail to identify structures defined largely by location, such as lobar parcellations and certain subcortical structures. Conversely, multi-template label fusion methods are excellent for structures defined largely by location, but perform poorly when segmenting structures that cannot be accurately identified through a consensus of registered templates. METHODS: We propose here a novel multi-classifier fusion algorithm with the advantages of both types of segmentation strategy. We illustrate and validate this algorithm using a group of 14 expertly hand-labeled images. RESULTS: Our method generated segmentations of cortical and subcortical structures that were more similar to hand-drawn segmentations than majority vote label fusion or a recently published intensity/label fusion method. CONCLUSIONS: We have presented a novel, general segmentation algorithm with the advantages of both statistical classifiers and label fusion techniques.
PURPOSE: To develop an MRI segmentation method for brain tissues, regions, and substructures that yields improved classification accuracy. Current brain segmentation strategies include two complementary strategies. Multi-spectral classification techniques generate excellent segmentations for tissues with clear intensity contrast, but fail to identify structures defined largely by location, such as lobar parcellations and certain subcortical structures. Conversely, multi-template label fusion methods are excellent for structures defined largely by location, but perform poorly when segmenting structures that cannot be accurately identified through a consensus of registered templates. METHODS: We propose here a novel multi-classifier fusion algorithm with the advantages of both types of segmentation strategy. We illustrate and validate this algorithm using a group of 14 expertly hand-labeled images. RESULTS: Our method generated segmentations of cortical and subcortical structures that were more similar to hand-drawn segmentations than majority vote label fusion or a recently published intensity/label fusion method. CONCLUSIONS: We have presented a novel, general segmentation algorithm with the advantages of both statistical classifiers and label fusion techniques.
Authors: Mert R Sabuncu; B T Thomas Yeo; Koen Van Leemput; Bruce Fischl; Polina Golland Journal: IEEE Trans Med Imaging Date: 2010-06-17 Impact factor: 10.048
Authors: Jyrki Mp Lötjönen; Robin Wolz; Juha R Koikkalainen; Lennart Thurfjell; Gunhild Waldemar; Hilkka Soininen; Daniel Rueckert Journal: Neuroimage Date: 2009-10-24 Impact factor: 6.556
Authors: Neil I Weisenfeld; Jurriaan M Peters; Peter T Tsai; Sanjay P Prabhu; Kira A Dies; Mustafa Sahin; Simon K Warfield Journal: Pediatr Neurol Date: 2013-02 Impact factor: 3.372