Automated lesion segmentation with BIANCA: Impact of population-level features, classification algorithm and locally adaptive thresholding

Sundaresan, Vaanathi; Zamboni, Giovanna; Heron, Campbell Le; Rothwell, Peter M.; Battaglini, Marco; Stefano, Nicola De; Jenkinson, Mark; Griffanti, Ludovica

doi:10.1016/j.neuroimage.2019.116056

Cited by 39 publications

(37 citation statements)

References 34 publications

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“…FLIRT (Qin et al, 2018;Griffanti et al, 2016;Guerrero et al, 2017;Sundaresan et al, 2019;Damangir et al, 2017;Rachmadi et al, 2018Rachmadi et al, , 2020Ghafoorian et al, 2017;Hong et al, 2020;Ding et al, 2020), three used ANTs (Schirmer et al, 2019;Manjón et al, 2018;Stone et al, 2016), five used SPM Roy et al, 2015;Jiang et al, 2018;Knight et al, 2018;Valverde et al, 2017), one study used elastix (Moskops et al, 2018), one study used 3DSlicer…”

Section: Pre-processing Methodsmentioning

confidence: 99%

“…Computing and Computer Assisted Intervention (MICCAI) segmentation challenges (Roy et al, 2015;Sudre et al, 2015;Van Obproek et al, 2015a, 2015bWang et al, 2015;Valverde et al, 2017;Zhan et al, 2017;Knight et al, 2018;Li et al, 2018;Manjón et al, 2018;Moeskops et al, 2018;Sundaresan et al, 2019;Wu, Zhang, Wang & Tang, 2019;Liu et al, 2020). Nine of them also used additional datasets or patient data from clinics.…”

Section: Sample Characteristicsmentioning

confidence: 99%

“…Nine of them also used additional datasets or patient data from clinics. Out of 37, twelve studies reported using data from prospective studies or clinics (Atlason et al, 2019;Bowles et al, 2017;Guerrero et al, 2017;Hong et al, 2020;Moeskops et al, 2018;Ling et al, 2018;Park et al, 2018;Qin et al, 2018;Rincón et al, 2017;Roy et al, 2015;Sundaresan et al, 2019;Wang et al, 2015). Four studies used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (Sudre et al, 2017;Dadar et al, 2017a;Rachmadi et al, 2018Rachmadi et al, , 2020, of which only two declared the subset used (Rachmadi et al, 2018(Rachmadi et al, , 2020.…”

Section: Sample Characteristicsmentioning

confidence: 99%

“…Data inclusion and exclusion criteria were not explained in 22/37 studies. Of the studies that reported demographic information, five recruited healthy controls (Griffanti et al, 2016;Sundaresan et al, 2019;Damangir et al, 2017;Rincón et al, 2017;Ding et al, 2020). One study stated that the data selection and manipulation were blinded to clinical information (i.e., avoided clinical review bias) (Dadar et al, 2017a).…”

Section: Risk Of Bias Assessment Within Studiesmentioning

confidence: 99%

“…Twelve studies used data only acquired at 1.5T, and twelve used data only acquired at 3T. Only 11/37 studies used data acquired at both 1.5T and 3T (Atlason et al, 2019;Dadar et al, 2017a;Jiang et al, 2018;Knight et al, 2018;Li et al, 2018;Ling et al, 2018;Liu et al, 2020;Manjón et al, 2018;Sundaresan et al, 2019;Wang et al, 2015;Wu, Zhang, Wang & Tang, 2019).…”

Section: Risk Of Bias Assessment Within Studiesmentioning

confidence: 99%

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

Balakrishnan

Hernández

Farrall

2020

Preprint

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Background: White matter hyperintensities (WMH), of presumed vascular origin, are visible and quantifiable neuroradiological markers of brain parenchymal change. These changes may range from damage secondary to inflammation and other neurological conditions, through to healthy ageing. Fully automatic WMH quantification methods are promising, but still, traditional semi-automatic methods seem to be preferred in clinical research. We systematically reviewed the literature for fully automatic methods developed in the last five years, to assess what are considered state-of-the-art techniques, as well as trends in the analysis of WMH of presumed vascular origin. Method: We registered the systematic review protocol with the International Prospective Register of Systematic Reviews (PROSPERO), registration number - CRD42019132200. We conducted the search for fully automatic methods developed from 2015 to July 2020 on Medline, Science direct, IEE Explore, and Web of Science. We assessed risk of bias and applicability of the studies using QUADAS 2. Results: The search yielded 2327 papers after removing 104 duplicates. After screening titles, abstracts and full text, 37 were selected for detailed analysis. Of these, 16 proposed a supervised segmentation method, 10 proposed an unsupervised segmentation method, and 11 proposed a deep learning segmentation method. Average DSC values ranged from 0.538 to 0.93, being the highest value obtained from a deep learning segmentation method. Only four studies validated their method in longitudinal samples, and eight performed an additional validation using clinical parameters. Only 8/37 studies made available their method in public repositories. Conclusions: Although deep learning methods reported highly accurate results, we found no evidence that favours them over the more established k-NN, linear regression and unsupervised methods in this task. Data and code availability, bias in study design and ground truth generation influence the wider validation and applicability of these methods in clinical research.

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Section: Pre-processing Methodsmentioning

confidence: 99%

Section: Sample Characteristicsmentioning

confidence: 99%

Section: Sample Characteristicsmentioning

confidence: 99%

Section: Risk Of Bias Assessment Within Studiesmentioning

confidence: 99%

Section: Risk Of Bias Assessment Within Studiesmentioning

confidence: 99%

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

Balakrishnan

Hernández

Farrall

2020

Preprint

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Mapping the interplay of atrial fibrillation, brain structure, and cognitive dysfunction

Petersen,

Chevalier,

Naegele

et al. 2024

Alzheimer's & Dementia

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INTRODUCTIONAtrial fibrillation (AF) is associated with an elevated risk of cognitive impairment and dementia. Understanding the cognitive sequelae and brain structural changes associated with AF is vital for addressing ensuing health care needs.METHODS AND RESULTSWe examined 1335 stroke‐free individuals with AF and 2683 matched controls using neuropsychological assessments and multimodal neuroimaging. The analysis revealed that individuals with AF exhibited deficits in executive function, processing speed, and reasoning, accompanied by reduced cortical thickness, elevated extracellular free‐water content, and widespread white matter abnormalities, indicative of small vessel pathology. Notably, brain structural differences statistically mediated the relationship between AF and cognitive performance.DISCUSSIONIntegrating a comprehensive analysis approach with extensive clinical and magnetic resonance imaging data, our study highlights small vessel pathology as a possible unifying link among AF, cognitive decline, and abnormal brain structure. These insights can inform diagnostic approaches and motivate the ongoing implementation of effective therapeutic strategies.Highlights We investigated neuropsychological and multimodal neuroimaging data of 1335 individuals with atrial fibrillation (AF) and 2683 matched controls. Our analysis revealed AF‐associated deficits in cognitive domains of attention, executive function, processing speed, and reasoning. Cognitive deficits in the AF group were accompanied by structural brain alterations including reduced cortical thickness and gray matter volume, alongside increased extracellular free‐water content as well as widespread differences of white matter integrity. Structural brain changes statistically mediated the link between AF and cognitive performance, emphasizing the potential of structural imaging markers as a diagnostic tool in AF‐related cognitive decline.

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Performance of three freely available methods for extracting white matter hyperintensities: FreeSurfer, UBO Detector, and BIANCA

Hotz

Deschwanden

Liem

et al. 2021

Human Brain Mapping

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White matter hyperintensities (WMH) of presumed vascular origin are frequently found in MRIs of healthy older adults. WMH are also associated with aging and cognitive decline. Here, we compared and validated three algorithms for WMH extraction: FreeSurfer (T1w), UBO Detector (T1w + FLAIR), and FSL's Brain Intensity AbNormality Classification Algorithm (BIANCA; T1w + FLAIR) using a longitudinal dataset comprising MRI data of cognitively healthy older adults (baseline N = 231, age range 64-87 years). As reference we manually segmented WMH in T1w, three-dimensional (3D) FLAIR, and two-dimensional (2D) FLAIR images which were used to assess the segmentation accuracy of the different automated algorithms. Further, we assessed the relationships of WMH volumes provided by the algorithms with Fazekas scores and age. FreeSurfer underestimated the WMH volumes and scored worst in Dice Similarity Coefficient (DSC = 0.434) but its WMH volumes strongly correlated with the Fazekas scores (r s = 0.73). BIANCA accomplished the highest DSC (0.602) in 3D FLAIR images. However, the relations with the Fazekas scores were only moderate, especially in the 2D FLAIR images (r s = 0.41), and many outlier WMH volumes were detected when exploring withinperson trajectories (2D FLAIR: ~30%). UBO Detector performed similarly to BIANCA in DSC with both modalities and reached the best DSC in 2D FLAIR (0.531) without requiring a tailored training dataset. In addition, it achieved very high associations with the Fazekas scores (2D FLAIR: r s = 0.80). In summary, our results emphasize the importance of carefully contemplating the choice of the WMH segmentation algorithm and MR-modality.

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Automated lesion segmentation with BIANCA: Impact of population-level features, classification algorithm and locally adaptive thresholding

Cited by 39 publications

References 34 publications

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

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

Mapping the interplay of atrial fibrillation, brain structure, and cognitive dysfunction

Performance of three freely available methods for extracting white matter hyperintensities: FreeSurfer, UBO Detector, and BIANCA

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