Knowledge Distillation for Brain Tumor Segmentation

Lachinov, Dmitrii; Shipunova, Elena; Турлапов, Вадим

doi:10.1007/978-3-030-46643-5_32

Cited by 11 publications

(6 citation statements)

References 10 publications

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“…It can be done automatically to cope with the time-consuming disadvantage of manual segmentation ( 29 , 30 ). Considering that the MRI images in our study were also used in the BraTS challenge and that the ground truth of tumor segmentation for patients in our cohort are not all available, we used the pre-trained model on the BraTS challenge 2019 to delineate the regions of tumor lesions, which achieved the accuracy of 90.45% on the validation set ( 31 ). In the BraTS 2019 dataset, all the samples in the training set are provided with four ground truth labels for 4 regions: background (label 0), necrotic and non-enhanced tumor (label 1), peritumoral edema (label 2), and enhanced tumor (label 4).…”

Section: Methodsmentioning

confidence: 99%

Multimodal MRI Image Decision Fusion-Based Network for Glioma Classification

et al. 2022

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PurposeGlioma is the most common primary brain tumor, with varying degrees of aggressiveness and prognosis. Accurate glioma classification is very important for treatment planning and prognosis prediction. The main purpose of this study is to design a novel effective algorithm for further improving the performance of glioma subtype classification using multimodal MRI images.MethodMRI images of four modalities for 221 glioma patients were collected from Computational Precision Medicine: Radiology-Pathology 2020 challenge, including T1, T2, T1ce, and fluid-attenuated inversion recovery (FLAIR) MRI images, to classify astrocytoma, oligodendroglioma, and glioblastoma. We proposed a multimodal MRI image decision fusion-based network for improving the glioma classification accuracy. First, the MRI images of each modality were input into a pre-trained tumor segmentation model to delineate the regions of tumor lesions. Then, the whole tumor regions were centrally clipped from original MRI images followed by max–min normalization. Subsequently, a deep learning-based network was designed based on a unified DenseNet structure, which extracts features through a series of dense blocks. After that, two fully connected layers were used to map the features into three glioma subtypes. During the training stage, we used the images of each modality after tumor segmentation to train the network to obtain its best accuracy on our testing set. During the inferring stage, a linear weighted module based on a decision fusion strategy was applied to assemble the predicted probabilities of the pre-trained models obtained in the training stage. Finally, the performance of our method was evaluated in terms of accuracy, area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), etc.ResultsThe proposed method achieved an accuracy of 0.878, an AUC of 0.902, a sensitivity of 0.772, a specificity of 0.930, a PPV of 0.862, an NPV of 0.949, and a Cohen’s Kappa of 0.773, which showed a significantly higher performance than existing state-of-the-art methods.ConclusionCompared with current studies, this study demonstrated the effectiveness and superiority in the overall performance of our proposed multimodal MRI image decision fusion-based network method for glioma subtype classification, which would be of enormous potential value in clinical practice.

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Section: Methodsmentioning

confidence: 99%

Multimodal MRI Image Decision Fusion-Based Network for Glioma Classification

et al. 2022

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“…[70] introduced brain parcellation atlas to produce a location prior information for tumor segmentation. Lachinov et al [75] ensembles two variant U-Net [59,97] and a cascaded U-Net [76]. The final ensemble result out-performs each single network 1-2%.…”

Section: Network Ensemblementioning

confidence: 99%

Deep learning based brain tumor segmentation: a survey

Liu

Tong

Chen

et al. 2022

Complex Intell. Syst.

103

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Brain tumor segmentation is one of the most challenging problems in medical image analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain tumor regions. In recent years, deep learning methods have shown promising performance in solving various computer vision problems, such as image classification, object detection and semantic segmentation. A number of deep learning based methods have been applied to brain tumor segmentation and achieved promising results. Considering the remarkable breakthroughs made by state-of-the-art technologies, we provide this survey with a comprehensive study of recently developed deep learning based brain tumor segmentation techniques. More than 150 scientific papers are selected and discussed in this survey, extensively covering technical aspects such as network architecture design, segmentation under imbalanced conditions, and multi-modality processes. We also provide insightful discussions for future development directions.

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“…Although the adversarial perturbation that we generated is applicable to many semantic segmentation methods, we selected a pretrained model from the work of Lachinov et al [28]. The goal of their research was to combine three U-net style neural networks and compare the performance between the ensemble model trained with the regular quantity of data and one single neural network trained with an additional quantity of data.…”

Section: The Targeted Modelmentioning

confidence: 99%

Adversarial Perturbation on MRI Modalities in Brain Tumor Segmentation

Cheng

Ji²

2020

IEEE Access

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Convolutional neural networks(CNNs) have been widely used by biomedical image segmentation applications. U-net, as a semantic segmentation method, has become a mainstream approach to brain tumor segmentation. However, the intrinsic vulnerability of CNNs also brings potential risks to all CNN-based applications, including semantic segmentation applications. In this paper, we create a universal adversarial perturbation and apply it on every modality in order to investigate how the adversarial perturbation affects each Magnetic Resonance Imaging(MRI) modality and the MRI images overall. We evaluate the performance when all four modalities are attacked and when one modality is attacked. The results show the following: 1) The adversarial perturbation affects the accuracy performance greatly, regardless of the size of the perturbation; 2) When only one modality is attacked, the network structure and the other three modalities provide some resistance to the adversarial perturbation; and 3) There are performance differences in different modalities, which are strongly related to the intensity distribution. T2 is least affected by the adversarial perturbation, while T1 and T1ce are more affected by the adversarial perturbation.

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Knowledge Distillation for Brain Tumor Segmentation

Cited by 11 publications

References 10 publications

Multimodal MRI Image Decision Fusion-Based Network for Glioma Classification

Multimodal MRI Image Decision Fusion-Based Network for Glioma Classification

Deep learning based brain tumor segmentation: a survey

Adversarial Perturbation on MRI Modalities in Brain Tumor Segmentation

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