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Accurate segmentation of brain tumors across multiple MRI sequences is essential for diagnosis, treatment planning, and clinical decision-making. In this paper, I propose a cutting-edge framework, named multi-modal graph convolution network (M2GCNet), to explore the relationships across different MR modalities, and address the challenge of brain tumor segmentation. The core of M2GCNet is the multi-modal graph convolution module (M2GCM), a pivotal component that represents MR modalities as graphs, with nodes corresponding to image pixels and edges capturing latent relationships between pixels. This graph-based representation enables the effective utilization of both local and global contextual information. Notably, M2GCM comprises two important modules: the spatial-wise graph convolution module (SGCM), adept at capturing extensive spatial dependencies among distinct regions within an image, and the channel-wise graph convolution module (CGCM), dedicated to modelling intricate contextual dependencies among different channels within the image. Additionally, acknowledging the intrinsic correlation present among different MR modalities, a multi-modal correlation loss function is introduced. This novel loss function aims to capture specific nonlinear relationships between correlated modality pairs, enhancing the model's ability to achieve accurate segmentation results. The experimental evaluation on two brain tumor datasets demonstrates the superiority of the proposed M2GCNet over other state-of-the-art segmentation methods. Furthermore, the proposed method paves the way for improved tumor diagnosis, multi-modal information fusion, and a deeper understanding of brain tumor pathology.

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Brain tumor is one of the most aggressive cancers in the world, accurate brain tumor segmentation plays a critical role in clinical diagnosis and treatment planning. Although deep learning models have presented remarkable success in medical segmentation, they can only obtain the segmentation map without capturing the segmentation uncertainty. To achieve accurate and safe clinical results, it is necessary to produce extra uncertainty maps to assist the subsequent segmentation revision. To this end, we propose to exploit the uncertainty quantification in the deep learning model and apply it to multi-modal brain tumor segmentation. In addition, we develop an effective attention-aware multi-modal fusion method to learn the complimentary feature information from the multiple MR modalities. First, a multi-encoder-based 3D U-Net is proposed to obtain the initial segmentation results. Then, an estimated Bayesian model is presented to measure the uncertainty of the initial segmentation results. Finally, the obtained uncertainty maps are integrated into a deep learning-based segmentation network, serving as an additional constraint information to further refine the segmentation results. The proposed network is evaluated on publicly available BraTS 2018 and BraTS 2019 datasets. The experimental results demonstrate that the proposed method outperforms the previous state-of-the-art methods on Dice score, Hausdorff distance and Sensitivity metrics. Furthermore, the proposed components could be easily applied to other network architectures and other computer vision fields.

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Brain tumor is one of the leading causes of cancer death. The high-grade brain tumors are easier to recurrent even after standard treatment. Therefore, developing a method to predict brain tumor recurrence location plays an important role in the treatment planning and it can potentially prolong patient's survival time. There is still little work to deal with this issue. In this paper, we present a deep learning-based brain tumor recurrence location prediction network. Since the dataset is usually small, we propose to use transfer learning to improve the prediction. We first train a multi-modal brain tumor segmentation network on the public dataset BraTS 2021. Then, the pre-trained encoder is transferred to our private dataset for extracting the rich semantic features. Following that, a multi-scale multi-channel feature fusion model and a nonlinear correlation learning module are developed to learn the effective features. The correlation between multi-channel features is modeled by a nonlinear equation. To measure the similarity between the distributions of original features of one modality and the estimated correlated features of another modality, we propose to use Kullback-Leibler divergence. Based on this divergence, a correlation loss function is designed to maximize the similarity between the two feature distributions. Finally, two decoders are constructed to jointly segment the present brain tumor and predict its future tumor recurrence location. To the best of our knowledge, this is the first work that can segment the present tumor and at the same time predict future tumor recurrence location, making the treatment planning more efficient and precise. The experimental results demonstrated the effectiveness of our proposed method to predict the brain tumor recurrence location from the limited dataset.

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Multimodal MR brain tumor segmentation is one of the hottest issues in the community of medical image processing. However, acquiring the complete set of MR modalities is not always possible in clinical practice, due to the acquisition protocols, image corruption, scanner availability, scanning cost or allergies to certain contrast materials. The missing information can cause some restraints to brain tumor diagnosis, monitoring, treatment planning and prognosis. Thus, it is highly desirable to develop brain tumor segmentation methods to address the missing modalities problem. Based on the recent advancements, in this review, we provide a detailed analysis of the missing modality issue in MR-based brain tumor segmentation. First, we briefly introduce the biomedical background concerning brain tumor, MR imaging techniques, and the current challenges in brain tumor segmentation. Then, we provide a taxonomy of the state-of-the-art methods with five categories, namely, image synthesis-based method, latent feature space-based model, multi-source correlation-based method, knowledge distillation-based method, and domain adaptation-based method. In addition, the principles, architectures, benefits and limitations are elaborated in each method. Following that, the corresponding datasets and widely used evaluation metrics are described. Finally, we analyze the current challenges and provide a prospect for future development trends. This review aims to provide readers with a thorough knowledge of the recent contributions in the field of brain tumor segmentation with missing modalities and suggest potential future directions.

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Magnetic Resonance Imaging (MRI) is a widely used imaging technique to assess brain tumor. Accurately segmenting brain tumor from MR images is the key to clinical diagnostics and treatment planning. In addition, multi-modal MR images can provide complementary information for accurate brain tumor segmentation. However, it's common to miss some imaging modalities in clinical practice. In this paper, we present a novel brain tumor segmentation algorithm with missing modalities. Since it exists a strong correlation between multi-modalities, a correlation model is proposed to specially represent the latent multi-source correlation. Thanks to the obtained correlation representation, the segmentation becomes more robust in the case of missing modality. First, the individual representation produced by each encoder is used to estimate the modality independent parameter. Then, the correlation model transforms all the individual representations to the latent multi-source correlation representations. Finally, the correlation representations across modalities are fused via attention mechanism into a shared representation to emphasize the most important features for segmentation. We evaluate our model on BraTS 2018 and BraTS 2019 dataset, it outperforms the current state-of-the-art methods and produces robust results when one or more modalities are missing.

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The coronavirus disease (COVID-19) pandemic has led to a devastating effect on the global public health. Computed Tomography (CT) is an effective tool in the screening of COVID-19. It is of great importance to rapidly and accurately segment COVID-19 from CT to help diagnostic and patient monitoring. In this paper, we propose a U-Net based segmentation network using attention mechanism. As not all the features extracted from the encoders are useful for segmentation, we propose to incorporate an attention mechanism including a spatial attention module and a channel attention module, to a U-Net architecture to re-weight the feature representation spatially and channel-wise to capture rich contextual relationships for better feature representation. In addition, the focal Tversky loss is introduced to deal with small lesion segmentation. The experiment results, evaluated on a COVID-19 CT segmentation dataset where 473 CT slices are available, demonstrate the proposed method can achieve an accurate and rapid segmentation result on COVID-19. The method takes only 0.29 second to segment a single CT slice. The obtained Dice Score and Hausdorff Distance are 83.1% and 18.8, respectively.

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This paper presents a 3D brain tumor segmentation network from multi-sequence MRI datasets based on deep learning. We propose a three-stage network: generating constraints, fusion under constraints and final segmentation. In the first stage, an initial 3D U-Net segmentation network is introduced to produce an additional context constraint for each tumor region. Under the obtained constraint, multi-sequence MRI are then fused using an attention mechanism to achieve three single tumor region segmentations. Considering the location relationship of the tumor regions, a new loss function is introduced to deal with the multiple class segmentation problem. Finally, a second 3D U-Net network is applied to combine and refine the three single prediction results. In each stage, only 8 initial filters are used, allowing to decrease significantly the number of parameters to be estimated. We evaluated our method on BraTS 2017 dataset. The results are promising in terms of dice score, hausdorff distance, and the amount of memory required for training.

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