RETRACTED ARTICLE: A new method for skull stripping in brain MRI using multistable cellular neural networks

Yılmaz, Büşra; Durdu, Akif; Emlik, Ganime Dilek

doi:10.1007/s00521-016-2834-2

Cited by 13 publications

(5 citation statements)

References 38 publications

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“…We compiled a dataset with 2000 healthy brain MR images retrieved from two public libraries, BrainWeb [ 51 ] and NAMIC [ 52 ], and one internationally renowned hospital. The modality type of all selected brain MR images is T1.…”

Section: Methodology and Experiments On Mask–rcnn For Skull Strippingmentioning

confidence: 99%

Fully Automated Skull Stripping from Brain Magnetic Resonance Images Using Mask RCNN-Based Deep Learning Neural Networks

Azam,

Tariq,

Shehzad

et al. 2023

Brain Sciences

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This research comprises experiments with a deep learning framework for fully automating the skull stripping from brain magnetic resonance (MR) images. Conventional techniques for segmentation have progressed to the extent of Convolutional Neural Networks (CNN). We proposed and experimented with a contemporary variant of the deep learning framework based on mask region convolutional neural network (Mask–RCNN) for all anatomical orientations of brain MR images. We trained the system from scratch to build a model for classification, detection, and segmentation. It is validated by images taken from three different datasets: BrainWeb; NAMIC, and a local hospital. We opted for purposive sampling to select 2000 images of T1 modality from data volumes followed by a multi-stage random sampling technique to segregate the dataset into three batches for training (75%), validation (15%), and testing (10%) respectively. We utilized a robust backbone architecture, namely ResNet–101 and Functional Pyramid Network (FPN), to achieve optimal performance with higher accuracy. We subjected the same data to two traditional methods, namely Brain Extraction Tools (BET) and Brain Surface Extraction (BSE), to compare their performance results. Our proposed method had higher mean average precision (mAP) = 93% and content validity index (CVI) = 0.95%, which were better than comparable methods. We contributed by training Mask–RCNN from scratch for generating reusable learning weights known as transfer learning. We contributed to methodological novelty by applying a pragmatic research lens, and used a mixed method triangulation technique to validate results on all anatomical modalities of brain MR images. Our proposed method improved the accuracy and precision of skull stripping by fully automating it and reducing its processing time and operational cost and reliance on technicians. This research study has also provided grounds for extending the work to the scale of explainable artificial intelligence (XAI).

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Section: Methodology and Experiments On Mask–rcnn For Skull Strippingmentioning

confidence: 99%

Fully Automated Skull Stripping from Brain Magnetic Resonance Images Using Mask RCNN-Based Deep Learning Neural Networks

Azam,

Tariq,

Shehzad

et al. 2023

Brain Sciences

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“…Yilmaz et al [113] performed skull stripping using a cellular neural network called the multistable cellular neural network on MRI for skull stripping (mCNN-MRI-SS). They proposed using contrast enhancement for preprocessing by a linear image combinations algorithm.…”

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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“…Examples of other mechanisms used to compliment 2D CNNs for brain extraction include artificial bee colony algorithms for feature determination [69] and active shape models [70].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Brain Extraction of Neurological FLAIR MRI for Multicenter, Multi-Disease Datasets

DiGregorio

2023

Preprint

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<p>Fluid-Attenuated Inversion Recovery (FLAIR) MRI has emerged as an important sequence for analyzing neurodegenerative diseases. Large-scale, automated cross- sectional and longitudinal cerebral biomarker analysis from FLAIR datasets could progress disease characterization and help determine optimal intervention times. Brain extraction is a critical preprocessing step to such analytical pipelines. Despite this, most automated brain extraction algorithms are designed for T1-weighted or multi-modal inputs. This thesis presents the development of a deep learning-based brain extraction tool designed specifically for FLAIR. The proposed method, a Multiple Resolution U-Net (MultiResUNet), obtained mean Dice Similarity Coefficient (DSC) scores exceeding 98% on multicenter, multi-disease datasets. This enabled downstream clinical analysis where FLAIR biomarkers statistically differentiated (p<0.01) healthy, Mild CognitiveImpairment(MCI), andAlzheimer’s Disease (AD) patients. This work demonstrates that FLAIR alone can be used for end-to-end analysis of large datasets, which can lower acquisition costs, simplify clinical translation, and reduce measurement error associated with multi-modal approaches.</p>

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RETRACTED ARTICLE: A new method for skull stripping in brain MRI using multistable cellular neural networks

Cited by 13 publications

References 38 publications

Fully Automated Skull Stripping from Brain Magnetic Resonance Images Using Mask RCNN-Based Deep Learning Neural Networks

Fully Automated Skull Stripping from Brain Magnetic Resonance Images Using Mask RCNN-Based Deep Learning Neural Networks

Conventional and Deep Learning Methods for Skull Stripping in Brain MRI

Brain Extraction of Neurological FLAIR MRI for Multicenter, Multi-Disease Datasets

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