Traumatic brain injuries could cause intracranial hemorrhage (ICH). ICH could
lead to disability or death if it is not accurately diagnosed and treated in a
time-sensitive procedure. The current clinical protocol to diagnose ICH is
examining Computerized Tomography (CT) scans by radiologists to detect ICH and
localize its regions. However, this process relies heavily on the availability
of an experienced radiologist. In this paper, we designed a study protocol to
collect a dataset of 82 CT scans of subjects with traumatic brain injury.
Later, the ICH regions were manually delineated in each slice by a consensus
decision of two radiologists. Recently, fully convolutional networks (FCN) have
shown to be successful in medical image segmentation. We developed a deep FCN,
called U-Net, to segment the ICH regions from the CT scans in a fully automated
manner. The method achieved a Dice coefficient of 0.31 for the ICH segmentation
based on 5-fold cross-validation. The dataset is publicly available online at
PhysioNet repository for future analysis and comparison.

本研究试图利用深度完全卷积神经网络自动地将颅内出血区域从 CT 扫描结果中区分出来，以取代目前需要有经验的放射科医师进行检查的方式。该方法在 5 重交叉验证中取得了 0.31 的 Dice 系数，可用于未来分析和比较。

基于深度卷积模型的颅内出血分割

Intracranial Hemorrhage Segmentation Using Deep Convolutional Model

Significant advances have been made towards building accurate automatic
segmentation systems for a variety of biomedical applications using machine
learning. However, the performance of these systems often degrades when they
are applied on new data that differ from the training data, for example, due to
variations in imaging protocols. Manually annotating new data for each test
domain is not a feasible solution. In this work we investigate unsupervised
domain adaptation using adversarial neural networks to train a segmentation
method which is more invariant to differences in the input data, and which does
not require any annotations on the test domain. Specifically, we learn
domain-invariant features by learning to counter an adversarial network, which
attempts to classify the domain of the input data by observing the activations
of the segmentation network. Furthermore, we propose a multi-connected domain
discriminator for improved adversarial training. Our system is evaluated using
two MR databases of subjects with traumatic brain injuries, acquired using
different scanners and imaging protocols. Using our unsupervised approach, we
obtain segmentation accuracies which are close to the upper bound of supervised
domain adaptation.

该研究使用对抗神经网络进行无监督的领域自适应来训练一种分割方法，此方法更加稳健，并且不需要对测试领域进行任何注释，该系统在对两种头部 CT 数据进行测试时，得到的分割精度非常接近于有监督领域自适应的上限。