Rui Wang1, Chao Li, Jie Wang, Xiaoer Wei, Yuehua Li, Yuemin Zhu, Su Zhang. 1. School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Room 123, 3 Teaching Building, No. 1954, Huashan Road, Shanghai, China, 200030.
Abstract
INTRODUCTION: This study aims to develop an automatic segmentation framework on the basis of extreme value distribution (EVD) for the detection and volumetric quantification of white matter hyperintensities (WMHs) on fluid-attenuated inversion recovery (FLAIR) images. METHODS: Two EVD-based segmentation methods, namely the Gumbel and Fréchet segmentation, were developed to detect WMHs on FLAIR (slice thickness = 5 mm; TR/TE/TI = 11,000/120/2,800 ms; flip angle = 90°) images. Another automatic segmentation method using a trimmed likelihood estimator (TLE) was implemented for comparison with our proposed segmentation framework. The performances of the three automatic segmentation methods were evaluated by comparing with the manual segmentation method. RESULTS: The Dice similarity coefficients (DSCs) of the two EVD-based segmentation methods were larger than those of the TLE-based segmentation method (Gumbel, 0.823 ± 0.063; Fréchet, 0.843 ± 0.057; TLE, 0.817 ± 0.068), demonstrating that the EVD-based segmentation outperformed the TLE-based segmentation. The Fréchet segmentation obtained larger DSCs on patients with moderate to severe lesion loads and a comparable performance on patients with mild lesion loads, indicating that the Fréchet segmentation was superior to the Gumbel segmentation. The Gumbel segmentation underestimated the lesion volumes of all patients, whereas the Fréchet and TLE-based segmentation methods obtained overestimated lesion volumes (Manual, 13.71 ± 14.02 cc; Gumbel, 12.73 ± 13.21 cc; Fréchet, 13.88 ± 13.96 cc; TLE, 13.54 ± 12.27 cc). Moreover, the EVD-based segmentation was demonstrated to be comparable to other state-of-the-art methods on a publicly available dataset. CONCLUSION: The proposed EVD-based segmentation framework is a promising, effective, and convenient tool for volumetric quantification and further study of WMHs in aging and dementia.
INTRODUCTION: This study aims to develop an automatic segmentation framework on the basis of extreme value distribution (EVD) for the detection and volumetric quantification of white matter hyperintensities (WMHs) on fluid-attenuated inversion recovery (FLAIR) images. METHODS: Two EVD-based segmentation methods, namely the Gumbel and Fréchet segmentation, were developed to detect WMHs on FLAIR (slice thickness = 5 mm; TR/TE/TI = 11,000/120/2,800 ms; flip angle = 90°) images. Another automatic segmentation method using a trimmed likelihood estimator (TLE) was implemented for comparison with our proposed segmentation framework. The performances of the three automatic segmentation methods were evaluated by comparing with the manual segmentation method. RESULTS: The Dice similarity coefficients (DSCs) of the two EVD-based segmentation methods were larger than those of the TLE-based segmentation method (Gumbel, 0.823 ± 0.063; Fréchet, 0.843 ± 0.057; TLE, 0.817 ± 0.068), demonstrating that the EVD-based segmentation outperformed the TLE-based segmentation. The Fréchet segmentation obtained larger DSCs on patients with moderate to severe lesion loads and a comparable performance on patients with mild lesion loads, indicating that the Fréchet segmentation was superior to the Gumbel segmentation. The Gumbel segmentation underestimated the lesion volumes of all patients, whereas the Fréchet and TLE-based segmentation methods obtained overestimated lesion volumes (Manual, 13.71 ± 14.02 cc; Gumbel, 12.73 ± 13.21 cc; Fréchet, 13.88 ± 13.96 cc; TLE, 13.54 ± 12.27 cc). Moreover, the EVD-based segmentation was demonstrated to be comparable to other state-of-the-art methods on a publicly available dataset. CONCLUSION: The proposed EVD-based segmentation framework is a promising, effective, and convenient tool for volumetric quantification and further study of WMHs in aging and dementia.
Authors: Ying Wu; Simon K Warfield; I Leng Tan; William M Wells; Dominik S Meier; Ronald A van Schijndel; Frederik Barkhof; Charles R G Guttmann Journal: Neuroimage Date: 2006-06-22 Impact factor: 6.556
Authors: Daniel García-Lorenzo; Sylvain Prima; Douglas L Arnold; D Louis Collins; Christian Barillot Journal: IEEE Trans Med Imaging Date: 2011-02-14 Impact factor: 10.048
Authors: Martijn D Steenwijk; Petra J W Pouwels; Marita Daams; Jan Willem van Dalen; Matthan W A Caan; Edo Richard; Frederik Barkhof; Hugo Vrenken Journal: Neuroimage Clin Date: 2013-10-14 Impact factor: 4.881
Authors: Rutger Heinen; Martijn D Steenwijk; Frederik Barkhof; J Matthijs Biesbroek; Wiesje M van der Flier; Hugo J Kuijf; Niels D Prins; Hugo Vrenken; Geert Jan Biessels; Jeroen de Bresser Journal: Sci Rep Date: 2019-11-14 Impact factor: 4.379