Multimodal MR images can provide complementary information for accurate brain
tumor segmentation. However, it's common to have missing imaging modalities in
clinical practice. Since there exists a strong correlation between multi
modalities, a novel correlation representation block is proposed to specially
discover the latent multi-source correlation. Thanks to the obtained
correlation representation, the segmentation becomes more robust in the case of
missing modalities. The model parameter estimation module first maps the
individual representation produced by each encoder to obtain independent
parameters, then, under these parameters, the correlation expression module
transforms all the individual representations to form a latent multi-source
correlation representation. Finally, the correlation representations across
modalities are fused via the attention mechanism into a shared representation
to emphasize the most important features for segmentation. We evaluate our
model on BraTS 2018 datasets, it outperforms the current state-of-the-art
method and produces robust results when one or more modalities are missing.

本文提出了一种新颖的相关性表示块，其可以在缺失模态的情况下发现潜在的多源相关性，并通过注意机制将模态间的相关性表示融合成一个共享表示，从而提高脑肿瘤分割的鲁棒性，并在 BraTS 2018 数据集上取得了最新的最佳表现。

通过潜在的多源相关表示进行缺失模态的脑肿瘤分割

Brain tumor segmentation with missing modalities via latent multi-source  correlation representation

Gliomas are the most common primary brain malignancies, with different
degrees of aggressiveness, variable prognosis and various heterogeneous
histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic
core, active and non-enhancing core. This intrinsic heterogeneity is also
portrayed in their radio-phenotype, as their sub-regions are depicted by
varying intensity profiles disseminated across multi-parametric magnetic
resonance imaging (mpMRI) scans, reflecting varying biological properties.
Their heterogeneous shape, extent, and location are some of the factors that
make these tumors difficult to resect, and in some cases inoperable. The amount
of resected tumor is a factor also considered in longitudinal scans, when
evaluating the apparent tumor for potential diagnosis of progression.
Furthermore, there is mounting evidence that accurate segmentation of the
various tumor sub-regions can offer the basis for quantitative image analysis
towards prediction of patient overall survival. This study assesses the
state-of-the-art machine learning (ML) methods used for brain tumor image
analysis in mpMRI scans, during the last seven instances of the International
Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we
focus on i) evaluating segmentations of the various glioma sub-regions in
pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue
of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO
criteria, and iii) predicting the overall survival from pre-operative mpMRI
scans of patients that underwent gross total resection. Finally, we investigate
the challenge of identifying the best ML algorithms for each of these tasks,
considering that apart from being diverse on each instance of the challenge,
the multi-institutional mpMRI BraTS dataset has also been a continuously
evolving/growing dataset.

本研究评估了近年来机器学习方法在脑瘤 mpMRI 扫描方面的应用，主要集中在评估各种脑瘤子区域的分割、预测肿瘤进展和预测患者的总体存活率等方面，考虑到数据集的多样性和不断演化，同时探讨了针对每个任务的最佳机器学习算法的挑战。

用于脑肿瘤分割、进展评估和总体生存预测的最佳机器学习算法识别（BRATS 挑战）

Identifying the Best Machine Learning Algorithms for Brain Tumor  Segmentation, Progression Assessment, and Overall Survival Prediction in the  BRATS Challenge

Brain tumor is considered as one of the deadliest and most common form of
cancer both in children and in adults. Consequently, determining the correct
type of brain tumor in early stages is of significant importance to devise a
precise treatment plan and predict patient's response to the adopted treatment.
In this regard, there has been a recent surge of interest in designing
Convolutional Neural Networks (CNNs) for the problem of brain tumor type
classification. However, CNNs typically require large amount of training data
and can not properly handle input transformations. Capsule networks (referred
to as CapsNets) are brand new machine learning architectures proposed very
recently to overcome these shortcomings of CNNs, and posed to revolutionize
deep learning solutions. Of particular interest to this work is that Capsule
networks are robust to rotation and affine transformation, and require far less
training data, which is the case for processing medical image datasets
including brain Magnetic Resonance Imaging (MRI) images. In this paper, we
focus to achieve the following four objectives: (i) Adopt and incorporate
CapsNets for the problem of brain tumor classification to design an improved
architecture which maximizes the accuracy of the classification problem at
hand; (ii) Investigate the over-fitting problem of CapsNets based on a real set
of MRI images; (iii) Explore whether or not CapsNets are capable of providing
better fit for the whole brain images or just the segmented tumor, and; (iv)
Develop a visualization paradigm for the output of the CapsNet to better
explain the learned features. Our results show that the proposed approach can
successfully overcome CNNs for the brain tumor classification problem.

本论文通过采用 Capsules 网络结构，解决了医学图像数据如 MRI 图像的处理问题，同时优化了神经网络的结构以提高大脑肿瘤分类问题的准确性，并解决过拟合问题。

基于胶囊网络的脑肿瘤类型分类

Brain Tumor Type Classification via Capsule Networks

A major challenge in brain tumor treatment planning and quantitative
evaluation is determination of the tumor extent. The noninvasive magnetic
resonance imaging (MRI) technique has emerged as a front-line diagnostic tool
for brain tumors without ionizing radiation. Manual segmentation of brain tumor
extent from 3D MRI volumes is a very time-consuming task and the performance is
highly relied on operator's experience. In this context, a reliable fully
automatic segmentation method for the brain tumor segmentation is necessary for
an efficient measurement of the tumor extent. In this study, we propose a fully
automatic method for brain tumor segmentation, which is developed using U-Net
based deep convolutional networks. Our method was evaluated on Multimodal Brain
Tumor Image Segmentation (BRATS 2015) datasets, which contain 220 high-grade
brain tumor and 54 low-grade tumor cases. Cross-validation has shown that our
method can obtain promising segmentation efficiently.

本文提出了一种使用 U-Net 基于深度卷积神经网络的完全自动化脑肿瘤分割方法，该方法在 Multimodal Brain Tumor Image Segmentation (BRATS 2015) 数据集上进行了测试，并表现出良好的分割性能。

基于 U-Net 全卷积网络的自动脑肿瘤检测和分割

Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully  Convolutional Networks

In this paper, we present a fully automatic brain tumor segmentation method
based on Deep Neural Networks (DNNs). The proposed networks are tailored to
glioblastomas (both low and high grade) pictured in MR images. By their very
nature, these tumors can appear anywhere in the brain and have almost any kind
of shape, size, and contrast. These reasons motivate our exploration of a
machine learning solution that exploits a flexible, high capacity DNN while
being extremely efficient. Here, we give a description of different model
choices that we've found to be necessary for obtaining competitive performance.
We explore in particular different architectures based on Convolutional Neural
Networks (CNN), i.e. DNNs specifically adapted to image data.
We present a novel CNN architecture which differs from those traditionally
used in computer vision. Our CNN exploits both local features as well as more
global contextual features simultaneously. Also, different from most
traditional uses of CNNs, our networks use a final layer that is a
convolutional implementation of a fully connected layer which allows a 40 fold
speed up. We also describe a 2-phase training procedure that allows us to
tackle difficulties related to the imbalance of tumor labels. Finally, we
explore a cascade architecture in which the output of a basic CNN is treated as
an additional source of information for a subsequent CNN. Results reported on
the 2013 BRATS test dataset reveal that our architecture improves over the
currently published state-of-the-art while being over 30 times faster.

该论文提出了一种基于深度神经网络的全自动脑肿瘤分割方法，利用卷积神经网络对肿瘤进行分析和识别从而改善传统方法的效能，得出的结果比现有的最优方法提升了 30 倍以上。