Brain tumor segmentation plays a pivotal role in medical image processing. In
this work, we aim to segment brain MRI volumes. 3D convolution neural networks
(CNN) such as 3D U-Net and V-Net employing 3D convolutions to capture the
correlation between adjacent slices have achieved impressive segmentation
results. However, these 3D CNN architectures come with high computational
overheads due to multiple layers of 3D convolutions, which may make these
models prohibitive for practical large-scale applications. To this end, we
propose a highly efficient 3D CNN to achieve real-time dense volumetric
segmentation. The network leverages the 3D multi-fiber unit which consists of
an ensemble of lightweight 3D convolutional networks to significantly reduce
the computational cost. Moreover, 3D dilated convolutions are used to build
multi-scale feature representations. Extensive experimental results on the
BraTS-2018 challenge dataset show that the proposed architecture greatly
reduces computation cost while maintaining high accuracy for brain tumor
segmentation. The source code can be found at
this https URL

本文介绍了一种高效的 3D 卷积神经网络，采用 3D multi-fiber unit 并使用 3D dilated convolutions 来构建多尺度特征表示，以达到实时密集体积分割的目的，并在 BraTS-2018 挑战数据集上的实验结果表明，该神经网络架构在高保真度的前提下大大降低了计算成本。

MRI 实时脑肿瘤分割的 3D 扩张多纤网络

3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in  MRI

Knowledge of whole heart anatomy is a prerequisite for many clinical
applications. Whole heart segmentation (WHS), which delineates substructures of
the heart, can be very valuable for modeling and analysis of the anatomy and
functions of the heart. However, automating this segmentation can be arduous
due to the large variation of the heart shape, and different image qualities of
the clinical data. To achieve this goal, a set of training data is generally
needed for constructing priors or for training. In addition, it is difficult to
perform comparisons between different methods, largely due to differences in
the datasets and evaluation metrics used. This manuscript presents the
methodologies and evaluation results for the WHS algorithms selected from the
submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge,
in conjunction with MICCAI 2017. The challenge provides 120 three-dimensional
cardiac images covering the whole heart, including 60 CT and 60 MRI volumes,
all acquired in clinical environments with manual delineation. Ten algorithms
for CT data and eleven algorithms for MRI data, submitted from twelve groups,
have been evaluated. The results show that many of the deep learning (DL) based
methods achieved high accuracy, even though the number of training datasets was
limited. A number of them also reported poor results in the blinded evaluation,
probably due to overfitting in their training. The conventional algorithms,
mainly based on multi-atlas segmentation, demonstrated robust and stable
performance, even though the accuracy is not as good as the best DL method in
CT segmentation. The challenge, including the provision of the annotated
training data and the blinded evaluation for submitted algorithms on the test
data, continues as an ongoing benchmarking resource via its homepage
(\url{www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mmwhs/}).

这篇论文介绍了参加 2017 年 MICCAI 举办的 Multi-Modality Whole Heart Segmentation 挑战赛中，由 12 个小组提交的 10 种针对 CT 数据和 11 种针对 MRI 数据的自动整个心脏分割算法，结果表明，使用深度学习算法的方法在有限的训练数据集上也可以取得较高准确率，而基于多阶段分割的传统算法表现稳定，但 CT 分割的准确度不如最好的深度学习方法。

多模态全心分割算法评估：一个开放挑战

Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An  Open-Access Grand Challenge

Convolutional Neural Networks (CNNs) have been recently employed to solve
problems from both the computer vision and medical image analysis fields.
Despite their popularity, most approaches are only able to process 2D images
while most medical data used in clinical practice consists of 3D volumes. In
this work we propose an approach to 3D image segmentation based on a
volumetric, fully convolutional, neural network. Our CNN is trained end-to-end
on MRI volumes depicting prostate, and learns to predict segmentation for the
whole volume at once. We introduce a novel objective function, that we optimise
during training, based on Dice coefficient. In this way we can deal with
situations where there is a strong imbalance between the number of foreground
and background voxels. To cope with the limited number of annotated volumes
available for training, we augment the data applying random non-linear
transformations and histogram matching. We show in our experimental evaluation
that our approach achieves good performances on challenging test data while
requiring only a fraction of the processing time needed by other previous
methods.

本文提出了一种基于全卷积神经网络的三维图像分割方法，通过优化 Dice 系数的目标函数，应对前景和背景体素数量极不平衡的情况，并通过数据增强技术提高数据样本的数量和多样性，实验结果表明该方法在肺部三维图像分割任务中取得了较好的性能。