Etruscan mirrors constitute a significant category within Etruscan art and,
therefore, undergo systematic examinations to obtain insights into ancient
times. A crucial aspect of their analysis involves the labor-intensive task of
manually tracing engravings from the backside. Additionally, this task is
inherently challenging due to the damage these mirrors have sustained,
introducing subjectivity into the process. We address these challenges by
automating the process through photometric-stereo scanning in conjunction with
deep segmentation networks which, however, requires effective usage of the
limited data at hand. We accomplish this by incorporating predictions on a
per-patch level, and various data augmentations, as well as exploring
self-supervised learning. Compared to our baseline, we improve predictive
performance w.r.t. the pseudo-F-Measure by around 16%. When assessing
performance on complete mirrors against a human baseline, our approach yields
quantitative similar performance to a human annotator and significantly
outperforms existing binarization methods. With our proposed methodology, we
streamline the annotation process, enhance its objectivity, and reduce overall
workload, offering a valuable contribution to the examination of these
historical artifacts and other non-traditional documents.

通过光电测色扫描和深度分割网络的自动化处理，我们改善了古代艺术中费时且受损镜子的图像追踪和二值化方法，减轻了人工劳动，提高了预测性能以及与人类标注者相似的量化结果。

勾勒界线：深度分割用于提取古埃特鲁里亚镜中的艺术

Drawing the Line: Deep Segmentation for Extracting Art from Ancient  Etruscan Mirrors

Purpose: To provide a simulation framework for routine neuroimaging test
data, which allows for "stress testing" of deep segmentation networks against
acquisition shifts that commonly occur in clinical practice for T2 weighted
(T2w) fluid attenuated inversion recovery (FLAIR) Magnetic Resonance Imaging
(MRI) protocols.
Approach: The approach simulates "acquisition shift derivatives" of MR images
based on MR signal equations. Experiments comprise the validation of the
simulated images by real MR scans and example stress tests on state-of-the-art
MS lesion segmentation networks to explore a generic model function to describe
the F1 score in dependence of the contrast-affecting sequence parameters echo
time (TE) and inversion time (TI).
Results: The differences between real and simulated images range up to 19 %
in gray and white matter for extreme parameter settings. For the segmentation
networks under test the F1 score dependency on TE and TI can be well described
by quadratic model functions (R^2 > 0.9). The coefficients of the model
functions indicate that changes of TE have more influence on the model
performance than TI.
Conclusions: We show that these deviations are in the range of values as may
be caused by erroneous or individual differences of relaxation times as
described by literature. The coefficients of the F1 model function allow for
quantitative comparison of the influences of TE and TI. Limitations arise
mainly from tissues with the low baseline signal (like CSF) and when the
protocol contains contrast-affecting measures that cannot be modelled due to
missing information in the DICOM header.

为常规神经影像测试数据提供一种模拟框架，以对抗在临床实践中常见的 T2 加权 FLAIR 磁共振成像协议中常发生的采集漂移，从而对深度分割网络进行 “压力测试”；通过模拟磁共振信号方程的 “采集漂移导数” 来模拟 MR 图像的获取变化，并通过真实 MR 扫描验证生成的图像，使用最先进的 MS 病变分割网络进行示例压力测试，以探索描述 F1 得分与序列参数回波时间（TE）和倒转时间（TI）之间依赖关系的通用模型函数。

应用仿真技术对 T2 Flair MR 图像进行采集漂移模拟，压力测试 AI 分割网络

Simulation of acquisition shifts in T2 Flair MR images to stress test AI  segmentation networks

Any policy-level decision-making procedure and academic research involving
the optimum use of resources for development and planning initiatives depends
on accurate population density statistics. The current cutting-edge datasets
offered by WorldPop and Meta do not succeed in achieving this aim for
developing nations like Pakistan; the inputs to their algorithms provide flawed
estimates that fail to capture the spatial and land-use dynamics. In order to
precisely estimate population counts at a resolution of 30 meters by 30 meters,
we use an accurate built settlement mask obtained using deep segmentation
networks and satellite imagery. The Points of Interest (POI) data is also used
to exclude non-residential areas.

通过使用深度分割网络和卫星图像，利用准确的建筑定居面具，我们以 30 米乘 30 米的分辨率准确估计人口数量，并利用兴趣点（POI）数据排除非居民区。