Brain MRI Tumor Segmentation with Adversarial Networks

Giacomello, Edoardo; Loiacono, Daniele; Mainardi, Luca

doi:10.1109/ijcnn48605.2020.9207220

Cited by 15 publications

(5 citation statements)

References 22 publications

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“…Image Segmentation Benchmark) is provided [13]. The brain MRI image dataset BraTS has been utilized in order to obtain the expected outcome of this proposed framework.…”

Section: Datasets Used In CD B Lnl the Proposed System Uses Two Diffe...mentioning

confidence: 99%

“…The outcome of this proposed framework achieved 99.45% of the average value of the F1 score. However, the proposed system accuracy outcome was compared with existing framework techniques, and it obtained nearly about 99.69% of accuracy rate, which is a better score in their segment [13]. The MRI-based planes like sagittal, axial, and coronal are involved in training and testing the proposed framework in order to classify the brain malignant image [14,15].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Computational and Mathematical Methods in Medicine Glioma Brain Tumor Detection and Classification Using Convolutional Neural Network

Saravanan

Kumar

Velliangiri

et al. 2022

Computational and Mathematical Methods in Medicine

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The classification of the brain tumor image is playing a vital role in the medical image domain, and it directly assists the clinicians to understand the severity and to take an appropriate solution. The magnetic resonance imaging tool is used to analyze the brain tissues and to examine the different portion of brain circumstance. We propose the convolutional neural network database learning along with neighboring network limitation (CDBLNL) technique for brain tumor image classification in medical image processing domain. The proposed system architecture is constructed with multilayer-based metadata learning, and they have integrated with CNN layer to deliver the accurate information. The metadata-based vector encoding is used, and the type of coding estimation for extra dimension is known as sparse. In order to maintain the supervised data in terms of geometric format, the atoms of neighboring limitation are built based on a well-structured k -neighbored network. The resultant of the proposed system is considerably strong and subjective for classification. The proposed system used two different datasets, such as BRATS and REMBRANDT, and the proposed brain MRI classification technique outcome is more efficient than the other existing techniques.

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“…Image Segmentation Benchmark) is provided [13]. The brain MRI image dataset BraTS has been utilized in order to obtain the expected outcome of this proposed framework.…”

Section: Datasets Used In CD B Lnl the Proposed System Uses Two Diffe...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computational and Mathematical Methods in Medicine Glioma Brain Tumor Detection and Classification Using Convolutional Neural Network

Saravanan

Kumar

Velliangiri

et al. 2022

Computational and Mathematical Methods in Medicine

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show abstract

“…Deep Learning (DL) methods are the state-of-the-art approach for tackling automatic medical image segmentation tasks, with the U-Net [5] being the most widely adopted network variation [6]. Currently, the DL based medical image segmentation literature focuses predominantly on network architecture and architectural modifications, such as the integration of residual, dense, or inception blocks, for achieving performance improvements with evaluation commonly conducted on a single dataset or restricted number of datasets [7,8,9,10,11,12,13,14,15]. However, in addition to network architecture, DL based automatic segmentation performance depends on further network training pipeline components and hyperparameters, for example, image resampling strategy, input image patch size, augmentation strategy etc.…”

Section: Introductionmentioning

confidence: 99%

Exploring advanced architectural variations of nnUNet

McConnell,

Ndipenoch,

Cao

et al. 2023

Neurocomputing

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“…This adversarial loss serves as an adaptively learned similarity measure between the predicted segmentation label maps and the annotated ground truth that improves localization accuracy while enforcing spatial contiguity at low contrast regions, including image boundaries. Various end-to-end adversarial neural networks (e.g., SegAN) have been proposed as stable and effective frameworks for automatic segmentation (SegAN) of organs such as the brain, chest, and abdomen, among others ( Frid-Adar et al, 2018 ; Giacomello et al, 2020 ; Xun et al, 2021 ; Zhu et al, 2021 ). Furthermore, a recent study by Chen et al (2022) showed that a GAN-based paradigm improved the robustness and generalizability of deep learning models like graph neural networks (GNNs).…”

Section: Introductionmentioning

confidence: 99%

FetalGAN: Automated Segmentation of Fetal Functional Brain MRI Using Deep Generative Adversarial Learning and Multi-Scale 3D U-Net

Asis-Cruz

Jose

et al. 2022

Front. Neurosci.

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An important step in the preprocessing of resting state functional magnetic resonance images (rs-fMRI) is the separation of brain from non-brain voxels. Widely used imaging tools such as FSL’s BET2 and AFNI’s 3dSkullStrip accomplish this task effectively in children and adults. In fetal functional brain imaging, however, the presence of maternal tissue around the brain coupled with the non-standard position of the fetal head limit the usefulness of these tools. Accurate brain masks are thus generated manually, a time-consuming and tedious process that slows down preprocessing of fetal rs-fMRI. Recently, deep learning-based segmentation models such as convolutional neural networks (CNNs) have been increasingly used for automated segmentation of medical images, including the fetal brain. Here, we propose a computationally efficient end-to-end generative adversarial neural network (GAN) for segmenting the fetal brain. This method, which we call FetalGAN, yielded whole brain masks that closely approximated the manually labeled ground truth. FetalGAN performed better than 3D U-Net model and BET2: FetalGAN, Dice score = 0.973 ± 0.013, precision = 0.977 ± 0.015; 3D U-Net, Dice score = 0.954 ± 0.054, precision = 0.967 ± 0.037; BET2, Dice score = 0.856 ± 0.084, precision = 0.758 ± 0.113. FetalGAN was also faster than 3D U-Net and the manual method (7.35 s vs. 10.25 s vs. ∼5 min/volume). To the best of our knowledge, this is the first successful implementation of 3D CNN with GAN on fetal fMRI brain images and represents a significant advance in fully automating processing of rs-MRI images.

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Brain MRI Tumor Segmentation with Adversarial Networks

Cited by 15 publications

References 22 publications

Computational and Mathematical Methods in Medicine Glioma Brain Tumor Detection and Classification Using Convolutional Neural Network

Computational and Mathematical Methods in Medicine Glioma Brain Tumor Detection and Classification Using Convolutional Neural Network

Exploring advanced architectural variations of nnUNet

FetalGAN: Automated Segmentation of Fetal Functional Brain MRI Using Deep Generative Adversarial Learning and Multi-Scale 3D U-Net

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