Convolutional neural networks for skull-stripping in brain MR imaging using silver standard masks

Lucena, Oeslle; Souza, Roberto; Rittner, Letícia; Frayne, Richard; Lotufo, Roberto de Alencar

doi:10.1016/j.artmed.2019.06.008

Cited by 41 publications

(57 citation statements)

References 47 publications

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“…Other reconstruction tasks include the correction of infant cortical surface [357] and EPID dosimetry correction of the cerebrospinal region [358]. Other outlier usages include synthesis of medical images [359], image superresolution [20], and data augmentation for enabling easier annotation of medical images [360]. Applications of U-net based models applied on canonical tasks other than segmentation tasks are summarized in Table 9.…”

Section: Other Canonical Tasks By U-netmentioning

confidence: 99%

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to Xrays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net's potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

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Section: Other Canonical Tasks By U-netmentioning

confidence: 99%

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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“…Lucena et al 174 . solved their problem of not having sufficient ‘gold standard’ manual annotations to train a deep learning model for skull stripping in brain MRI by generating so‐called ‘silver standard’ annotations by combining the results of eight non‐deep learning brain extraction methods.…”

Section: Methodsmentioning

confidence: 99%

A review of medical image data augmentation techniques for deep learning applications

et al. 2021

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Summary Research in artificial intelligence for radiology and radiotherapy has recently become increasingly reliant on the use of deep learning‐based algorithms. While the performance of the models which these algorithms produce can significantly outperform more traditional machine learning methods, they do rely on larger datasets being available for training. To address this issue, data augmentation has become a popular method for increasing the size of a training dataset, particularly in fields where large datasets aren’t typically available, which is often the case when working with medical images. Data augmentation aims to generate additional data which is used to train the model and has been shown to improve performance when validated on a separate unseen dataset. This approach has become commonplace so to help understand the types of data augmentation techniques used in state‐of‐the‐art deep learning models, we conducted a systematic review of the literature where data augmentation was utilised on medical images (limited to CT and MRI) to train a deep learning model. Articles were categorised into basic, deformable, deep learning or other data augmentation techniques. As artificial intelligence models trained using augmented data make their way into the clinic, this review aims to give an insight to these techniques and confidence in the validity of the models produced.

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“…Lucena et al [110] proposed a network consisting of two main parts. The first part is a network having three 2D CNNs, and the second part is a network handling context variation.…”

Section: D Skull-stripping Methodsmentioning

confidence: 99%

Conventional and Deep Learning Methods for Skull Stripping in Brain MRI

2020

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Skull stripping in brain magnetic resonance volume has recently been attracting attention due to an increased demand to develop an efficient, accurate, and general algorithm for diverse datasets of the brain. Accurate skull stripping is a critical step for neuroimaging diagnostic systems because neither the inclusion of non-brain tissues nor removal of brain parts can be corrected in subsequent steps, which results in unfixed error through subsequent analysis. The objective of this review article is to give a comprehensive overview of skull stripping approaches, including recent deep learning-based approaches. In this paper, the current methods of skull stripping have been divided into two distinct groups—conventional or classical approaches, and convolutional neural networks or deep learning approaches. The potentials of several methods are emphasized because they can be applied to standard clinical imaging protocols. Finally, current trends and future developments are addressed giving special attention to recent deep learning algorithms.

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Convolutional neural networks for skull-stripping in brain MR imaging using silver standard masks

Cited by 41 publications

References 47 publications

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

A review of medical image data augmentation techniques for deep learning applications

Conventional and Deep Learning Methods for Skull Stripping in Brain MRI

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