Quantification of cerebral white matter hyperintensities (WMH) of presumed
vascular origin is of key importance in many neurological research studies.
Currently, measurements are often still obtained from manual segmentations on
brain MR images, which is a laborious procedure. Automatic WMH segmentation
methods exist, but a standardized comparison of the performance of such methods
is lacking. We organized a scientific challenge, in which developers could
evaluate their method on a standardized multi-center/-scanner image dataset,
giving an objective comparison: the WMH Segmentation Challenge
(this https URL).
Sixty T1+FLAIR images from three MR scanners were released with manual WMH
segmentations for training. A test set of 110 images from five MR scanners was
used for evaluation. Segmentation methods had to be containerized and submitted
to the challenge organizers. Five evaluation metrics were used to rank the
methods: (1) Dice similarity coefficient, (2) modified Hausdorff distance (95th
percentile), (3) absolute log-transformed volume difference, (4) sensitivity
for detecting individual lesions, and (5) F1-score for individual lesions.
Additionally, methods were ranked on their inter-scanner robustness.
Twenty participants submitted their method for evaluation. This paper
provides a detailed analysis of the results. In brief, there is a cluster of
four methods that rank significantly better than the other methods, with one
clear winner. The inter-scanner robustness ranking shows that not all methods
generalize to unseen scanners.
The challenge remains open for future submissions and provides a public
platform for method evaluation.

本文介绍了通过 WMH Segmentation Challenge，针对来自多个中心和扫描仪的脑部 MRI 图像，评估了五种指标来比较自动化白质高信号 （WMH）分割算法的性能，发现了四种方法的结果比其他方法显著优越，并且表明不是所有方法都适用于未知扫描仪。

自动分割白质高信号区的标准化评估及其分割挑战结果

Standardized Assessment of Automatic Segmentation of White Matter  Hyperintensities and Results of the WMH Segmentation Challenge

Chronic obstructive pulmonary disease (COPD) is a lung disease which can be
quantified using chest computed tomography (CT) scans. Recent studies have
shown that COPD can be automatically diagnosed using weakly supervised learning
of intensity and texture distributions. However, up till now such classifiers
have only been evaluated on scans from a single domain, and it is unclear
whether they would generalize across domains, such as different scanners or
scanning protocols. To address this problem, we investigate classification of
COPD in a multi-center dataset with a total of 803 scans from three different
centers, four different scanners, with heterogenous subject distributions. Our
method is based on Gaussian texture features, and a weighted logistic
classifier, which increases the weights of samples similar to the test data. We
show that Gaussian texture features outperform intensity features previously
used in multi-center classification tasks. We also show that a weighting
strategy based on a classifier that is trained to discriminate between scans
from different domains, can further improve the results. To encourage further
research into transfer learning methods for classification of COPD, upon
acceptance of the paper we will release two feature datasets used in this study
on this http URL

通过多中心数据集，使用高斯纹理特征和加权逻辑分类器诊断慢性阻塞性肺疾病 (COPD)，并发现其优于已有的弱监督学习方法。