An open, multi-vendor, multi-field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement

Souza, Roberto; Lucena, Oeslle; Garrafa, Julia; Gobbi, David G.; Saluzzi, Marina; Appenzeller, Simone; Rittner, Letícia; Frayne, Richard; Lotufo, Roberto de Alencar

doi:10.1016/j.neuroimage.2017.08.021

Cited by 180 publications

(107 citation statements)

References 25 publications

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“…Four different datasets including the MRI data from a prospective randomized Phase II and III trials in neuro-oncology (EORTC-26101) (Wick et al, 2017;Wick et al, 2016) (Puccio, Pooley, Pellman, Taverna, & Craddock, 2016;Shattuck et al, 2008;Souza et al, 2018) were used for the present study. The characteristics of the individual datasets were as follows.…”

Section: Datasetsmentioning

confidence: 99%

“…Manual segmentation is currently considered the "gold-standard" for brain extraction (Smith, 2002;Souza et al, 2018). However, this approach is not only very labor-intensive and time-consuming, but also shows a strong interindividual and intraindividual variability (Kleesiek et al, 2016;Smith, 2002;Souza et al, 2018) that could ultimately bias the analysis and consequently hamper the reproducibility of clinical studies. To overcome these shortcomings, several (semi-) automated brain extraction algorithms have been developed and optimized over the last years (Kalavathi & Prasath, 2016).…”

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confidence: 99%

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Automated brain extraction of multisequence MRI using artificial neural networks

Isensee

Schell

Pflueger

et al. 2019

Human Brain Mapping

423

253

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Brain extraction is a critical preprocessing step in the analysis of neuroimaging studies conducted with magnetic resonance imaging (MRI) and influences the accuracy of downstream analyses. The majority of brain extraction algorithms are, however, optimized for processing healthy brains and thus frequently fail in the presence of pathologically altered brain or when applied to heterogeneous MRI datasets. Here we introduce a new, rigorously validated algorithm (termed HD‐BET) relying on artificial neural networks that aim to overcome these limitations. We demonstrate that HD‐BET outperforms six popular, publicly available brain extraction algorithms in several large‐scale neuroimaging datasets, including one from a prospective multicentric trial in neuro‐oncology, yielding state‐of‐the‐art performance with median improvements of +1.16 to +2.50 points for the Dice coefficient and −0.66 to −2.51 mm for the Hausdorff distance. Importantly, the HD‐BET algorithm, which shows robust performance in the presence of pathology or treatment‐induced tissue alterations, is applicable to a broad range of MRI sequence types and is not influenced by variations in MRI hardware and acquisition parameters encountered in both research and clinical practice. For broader accessibility, the HD‐BET prediction algorithm is made freely available (http://www.neuroAI-HD.org) and may become an essential component for robust, automated, high‐throughput processing of MRI neuroimaging data.

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Section: Datasetsmentioning

confidence: 99%

mentioning

confidence: 99%

Automated brain extraction of multisequence MRI using artificial neural networks

Isensee

Schell

Pflueger

et al. 2019

Human Brain Mapping

423

253

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show abstract

“…We additionally validated the proposed method using a set of 12 publicly available labeled images available from Souza et al (2017). The brain masks were manually generated from scratch, and the dataset The performance of the proposed method, both before and after applying error correction, is shown in Table 5.…”

Section: Independent Validationmentioning

confidence: 99%

“…We additionally validate our proposed method on a secondary multicentre publicly available dataset (Souza et al, 2017).…”

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An efficient and accurate method for robust inter‐dataset brain extraction and comparisons with 9 other methods

Novosad

Collins

Initiative³

2018

Human Brain Mapping

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Brain extraction is an important first step in many magnetic resonance neuroimaging studies. Due to variability in brain morphology and in the appearance of the brain due to differences in scanner acquisition parameters, the development of a generally applicable brain extraction algorithm has proven challenging. Learning-based brain extraction algorithms in particular perform well when the target and training images are sufficiently similar, but often perform worse when this condition is not met. In this study, we propose a new patch-based multi-atlas segmentation method for brain extraction which is specifically developed for accurate and robust processing across datasets. Using a diverse collection of labeled images from 5 different datasets, extensive comparisons were made with 9 other commonly used brain extraction methods, both before and after applying error correction (a machine learning method for automatically correcting segmentation errors) to each method. The proposed method performed equal to or better than the other methods in each of two segmentation scenarios: a challenging inter-dataset segmentation scenario in which no dataset-specific atlases were used (mean Dice coefficient 98.57%, volumetric correlation 0.994 across datasets following error correction), and an intra-dataset segmentation scenario in which only dataset-specific atlases were used (mean Dice coefficient 99.02%, volumetric correlation 0.998 across datasets following error correction). Furthermore, combined with error correction, the proposed method runs in less than one-tenth of the time required by the other top-performing methods in the challenging inter-dataset comparisons. Validation on an independent multi-centre dataset also confirmed the excellent performance of the proposed method.

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3D Volume Rendering of MRI Images for Tumor Detection and Segmentation using nnUnet

2024

Artificial and Cognitive Computing for Sustainable Healthcare Systems in Smart Cities

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An open, multi-vendor, multi-field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement

Cited by 180 publications

References 25 publications

Automated brain extraction of multisequence MRI using artificial neural networks

Automated brain extraction of multisequence MRI using artificial neural networks

An efficient and accurate method for robust inter‐dataset brain extraction and comparisons with 9 other methods

3D Volume Rendering of MRI Images for Tumor Detection and Segmentation using nnUnet

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