The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE)
Challenge compares the performance of algorithms at predicting future evolution
of individuals at risk of Alzheimer's disease. TADPOLE Challenge participants
train their models and algorithms on historical data from the Alzheimer's
Disease Neuroimaging Initiative (ADNI) study or any other datasets to which
they have access. Participants are then required to make monthly forecasts over
a period of 5 years from January 2018, of three key outcomes for ADNI-3
rollover participants: clinical diagnosis, Alzheimer's Disease Assessment Scale
Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. These
individual forecasts are later compared with the corresponding future
measurements in ADNI-3 (obtained after the TADPOLE submission deadline). The
first submission phase of TADPOLE was open for prize-eligible submissions
between 15 June and 15 November 2017. The submission system remains open via
the website: this https URL, although since 15 November
2017 submissions are not eligible for the first round of prizes. This paper
describes the design of the TADPOLE Challenge.

该论文描述了 TADPOLE Challenge 的设计，该挑战旨在比较算法在预测患有阿尔茨海默病个体未来演变方面的表现。

TADPOLE 挑战赛：预测阿尔茨海默病的纵向演变

TADPOLE Challenge: Prediction of Longitudinal Evolution in Alzheimer's  Disease

This report provides an overview of the current state of the art deep
learning architectures and optimisation techniques, and uses the ADNI
hippocampus MRI dataset as an example to compare the effectiveness and
efficiency of different convolutional architectures on the task of patch-based
3-dimensional hippocampal segmentation, which is important in the diagnosis of
Alzheimer's Disease. We found that a slightly unconventional "stacked 2D"
approach provides much better classification performance than simple 2D patches
without requiring significantly more computational power. We also examined the
popular "tri-planar" approach used in some recently published studies, and
found that it provides much better results than the 2D approaches, but also
with a moderate increase in computational power requirement. Finally, we
evaluated a full 3D convolutional architecture, and found that it provides
marginally better results than the tri-planar approach, but at the cost of a
very significant increase in computational power requirement.

通过对 ADNI 海马 MRI 数据集进行分割实验，比较了不同的三维海马分割卷积架构的有效性和效率，结果表明，与简单的二维块相比，稍微非传统的 “堆叠的 2D” 方法提供了更好的分类性能，并且不需要额外的计算能力。同样我们也测试了一种流行的 “三平面” 方法，发现它提供了比 2D 方法更好的结果，但需要适度增加计算能力的要求。最后，我们评估了完整的三维卷积体系结构，并发现它提供了比三平面方法略好的结果，但代价是需要非常大的计算能力要求。