Purpose: To develop a method for automated segmentation of hypothalamus
subregions informed by ultra-high resolution ex vivo magnetic resonance images
(MRI), which generalizes across MRI sequences and resolutions without
retraining.
Materials and Methods: We trained our deep learning method, H-synEx, with
synthetic images derived from label maps built from ultra-high resolution ex
vivo MRI scans, which enables finer-grained manual segmentation when compared
with 1mm isometric in vivo images. We validated this retrospective study using
1535 in vivo images from six datasets and six MRI sequences. The quantitative
evaluation used the Dice Coefficient (DC) and Average Hausdorff distance (AVD).
Statistical analysis compared hypothalamic subregion volumes in controls,
Alzheimer's disease (AD), and behavioral variant frontotemporal dementia
(bvFTD) subjects using the area under the curve (AUC) and Wilcoxon rank sum
test.
Results: H-SynEx can segment the hypothalamus across various MRI sequences,
encompassing FLAIR sequences with significant slice spacing (5mm). Using
hypothalamic volumes on T1w images to distinguish control from AD and bvFTD
patients, we observed AUC values of 0.74 and 0.79 respectively. Additionally,
AUC=0.66 was found for volume variation on FLAIR scans when comparing control
and non-patients.
Conclusion: Our results show that H-SynEx successfully leverages information
from ultra-high resolution scans to segment in vivo from different MRI
sequences such as T1w, T2w, PD, qT1, FA, and FLAIR. We also found that our
automated segmentation was able to discriminate controls versus patients on
FLAIR images with 5mm spacing. H-SynEx is openly available at
this https URL

开发了一种基于超高分辨率离体磁共振成像（MRI）的下丘脑亚区自动分割方法，可跨 MRI 序列和分辨率进行普适化，无需重新训练。

H-SynEx：使用合成图像和超高分辨率体外 MRI 进行下丘脑亚区分割

H-SynEx: Using synthetic images and ultra-high resolution ex vivo MRI  for hypothalamus subregion segmentation

We introduce MatSynth, a dataset of $4,000+$ CC0 ultra-high resolution PBR
materials. Materials are crucial components of virtual relightable assets,
defining the interaction of light at the surface of geometries. Given their
importance, significant research effort was dedicated to their representation,
creation and acquisition. However, in the past 6 years, most research in
material acquisiton or generation relied either on the same unique dataset, or
on company-owned huge library of procedural materials. With this dataset we
propose a significantly larger, more diverse, and higher resolution set of
materials than previously publicly available. We carefully discuss the data
collection process and demonstrate the benefits of this dataset on material
acquisition and generation applications. The complete data further contains
metadata with each material's origin, license, category, tags, creation method
and, when available, descriptions and physical size, as well as 3M+ renderings
of the augmented materials, in 1K, under various environment lightings. The
MatSynth dataset is released through the project page at:
this https URL

通过介绍 MatSynth 数据集，本文提出了一个比以前公开的数据集更大、更多样化且更高分辨率的材质数据集，详细讨论了数据采集过程，并展示了该数据集在材质获取和生成应用中的优势。

MatSynth：现代 PBR 材质数据集

MatSynth: A Modern PBR Materials Dataset

With the increasing interest and rapid development of methods for Ultra-High
Resolution (UHR) segmentation, a large-scale benchmark covering a wide range of
scenes with full fine-grained dense annotations is urgently needed to
facilitate the field. To this end, the URUR dataset is introduced, in the
meaning of Ultra-High Resolution dataset with Ultra-Rich Context. As the name
suggests, URUR contains amounts of images with high enough resolution (3,008
images of size 5,120x5,120), a wide range of complex scenes (from 63 cities),
rich-enough context (1 million instances with 8 categories) and fine-grained
annotations (about 80 billion manually annotated pixels), which is far superior
to all the existing UHR datasets including DeepGlobe, Inria Aerial, UDD, etc..
Moreover, we also propose WSDNet, a more efficient and effective framework for
UHR segmentation especially with ultra-rich context. Specifically, multi-level
Discrete Wavelet Transform (DWT) is naturally integrated to release computation
burden while preserve more spatial details, along with a Wavelet Smooth Loss
(WSL) to reconstruct original structured context and texture with a smooth
constrain. Experiments on several UHR datasets demonstrate its state-of-the-art
performance. The dataset is available at this https URL

介绍一种包含高分辨率图像，丰富场景上下文和细致注释的大规模数据集 URUR，以及一个更高效和有效的含有离散小波变换和小波平滑损失的 UHR 语义分割框架 WSDNet。

具有超丰富背景的超高分辨率分割：一项新的基准测试

Ultra-High Resolution Segmentation with Ultra-Rich Context: A Novel  Benchmark

Ultra-high resolution image segmentation has raised increasing interests in
recent years due to its realistic applications. In this paper, we innovate the
widely used high-resolution image segmentation pipeline, in which an ultra-high
resolution image is partitioned into regular patches for local segmentation and
then the local results are merged into a high-resolution semantic mask. In
particular, we introduce a novel locality-aware context fusion based
segmentation model to process local patches, where the relevance between local
patch and its various contexts are jointly and complementarily utilized to
handle the semantic regions with large variations. Additionally, we present the
alternating local enhancement module that restricts the negative impact of
redundant information introduced from the contexts, and thus is endowed with
the ability of fixing the locality-aware features to produce refined results.
Furthermore, in comprehensive experiments, we demonstrate that our model
outperforms other state-of-the-art methods in public benchmarks. Our released
codes are available at: this https URL

本文介绍了一种新的局部感知上下文融合图像分割模型，旨在处理具有大变化语义区域的超高分辨率图像，并通过交替本地增强模块限制冗余信息的负面影响以产生精细结果，实验结果表明其优于其他最先进的方法。