Automatic method for white matter lesion segmentation based on T1‐fluid‐attenuated inversion recovery images

Abstract

The authors propose a fast and effective solution for automatic segmentation of white matter lesions by using T1 and fluid‐attenuated inversion recovery (FLAIR) image modalities with no need for manual segmentation and atlas registration. Initially, a brain tissue segmentation method is used to segment the T1 image into cerebrospinal fluid (CSF), grey matter and white matter. Based on the obtained tissue segmentation results, the region of interest (ROI) of the FLAIR image is created by subtracting the CSF from the FLAIR image. Subsequently, the authors calculate the z‐score of the intensities in the ROI and define a threshold to perform a preliminary identification of abnormalities from normal tissues. The abnormalities obtained at this stage are used as the prior knowledge for the modified level‐set technique. The proposed level set method here is applied based on local Gaussian distribution to precisely detect the boundaries of the white matter lesions in the ROI. The level set method based on local Gaussian distribution fitting energy is robust to the intensity inhomogeneity of MR data and therefore capable of precisely extracting the boundaries of white matter lesions. Experimental analysis and quantitative comparisons with the peak‐seeking and state‐of‐the‐art white matter lesion segmentation (WMLS) techniques demonstrate that the algorithm is a stable and effective approach which significantly outperforms other trusted solutions for white matter lesion segmentation.

Automatic Segmentation of White Matter Lesions Using SVM and RSF Model in Multi-channel MRI

“…Our method was examined on ACCORD-MIND MRI dataset [15]. We compare our algorithm with the WMLs segmentation algorithm developed by Lao et al [2], Lesion Segmentation Toolbox (LST) [16], LesionTOADS [17], Zhan et al [18]. Figures 2 and 3 show the automatic segmentation versus manual lesion segmentation in different sizes of lesion burden.…”

Automatic Segmentation of White Matter Hyperintensities from Brain Magnetic Resonance Images in the Era of Deep Learning and Big Data – A Systematic Review

“…Intensity inhomogeneity correction was reported in 17/37 studies, and it was always performed using a well-known tool: N3 (or N4), SPM, FSL-FAST or the Nu estimate. N3 and its newer version N4, were the tools most commonly used for intensity inhomogeneity correction (Stone et al, 2016;Bowles, et al, 2017;Dadar et al, 2017a;Van Opbroek, Ikram, Vernooij & de Bruijne, 2015a, 2015bDamangir et al, 2017;Roy et al, 2015;Wang et al, 2015;Zhan et al, 2015;Zhan et al, 2017;Atlason et al, 2019;Ding et al, 2020). Non-local means was the only filtering technique used by the two studies that reported having included noise removal within their preprocessing steps (Manjón et al, 2018;Dadar et al, 2017a).…”

“…In total, eleven studies used Convolutional Neural Networks (Rachmadi, et al, 2018;Li et al, 2018;Guerrero et al, 2017;Moeskops et al, 2018;Ghafoorian et al, 2016;Hong et al, 2020;Manjón et al, 2018;Liu et al, 2020;Diniz et al, 2018;Schirmer et al, 2019), four studies proposed a method based on k-nearest neighbours (k-NN) (Sundaresan et al, 2019;Jiang et al, 2018;Ling et al, 2018;Griffanti et al, 2016), four studies proposed regression models (Knight et al, 2018;Dadar et al, 2017a;Zhan et al, 2017;Ding et al, 2020), and three studies used Random forest (RF) in their proposed algorithms (Stone et al, 2016;Park et al, 2018;Roy et al, 2015). Two studies proposed a method based on Fuzzy C mean algorithm (Zhan et al, 2015;Valverde et al, 2017) and three proposed improvements to a Gaussian Mixture Model framework (Sudre et al, 2015(Sudre et al, , 2017Fiford et al, 2020), both unsupervised methods. We summarize the segmentation methods included in the reviewed studies in Figure 4 (right hand side).…”

“…Damangir et al 2017developed an unsupervised method that statistically defined WMH based on the one-tailed Kolmogorov-Smirnov test (Gail et al, 1976). Zhan et al (2015) present an unsupervised WMH segmentation method for T1 and FLAIR data.…”

Scope of Improvement in Quality of Diagnostic Images for Pelvic region

“…Zhan et al [1] adopted Level set method which included gaussian distribution function for enhancement of visualization of the lesion tissue whereas it also faced a drawback of Inability to handle complex structures of lesions under low quality. Harmouche et al [2] developed the methodology for classification of multiple sclerosis lesions using markov random field.…”