Our method builds upon previous Medical Out-of-Distribution (MOOD) challenge
winners that empirically show that synthetic local anomalies generated copying
/ interpolating foreign patches are useful to train segmentation networks able
to generalize to unseen types of anomalies. In terms of the synthetic anomaly
generation process, our contributions makes synthetic anomalies more
heterogeneous and challenging by 1) using random shapes instead of squares and
2) smoothing the interpolation edge of anomalies so networks cannot rely on the
high gradient between image - foreign patch to identify anomalies. Our
experiments using the validation set of 2020 MOOD winners show that both
contributions improved substantially the method performance. We used a standard
3D U-Net architecture as segmentation network, trained patch-wise in both brain
and abdominal datasets. Our final challenge submission consisted of 10 U-Nets
trained across 5 data folds with different configurations of the anomaly
generation process. Our method achieved first position in both sample-wise and
pixel-wise tasks in the 2022 edition of the Medical Out-of-Distribution held at
MICCAI.

我们的研究基于之前的医学领域离群分布挑战（MOOD）的获奖者的工作，其经验性地表明通过复制和插值外来补丁生成合成局部异常有助于训练分割网络，从而能够推广到未见过的异常类型。我们的贡献在合成异常生成过程中，通过使用随机形状而不是方形，并平滑异常的插值边缘，使合成异常更加多样化和具有挑战性，使网络无法仅仅依赖图像 - 外来补丁之间的高梯度来识别异常。我们的实验证明，这两项贡献显著改进了该方法的性能。我们使用标准的 3D U-Net 架构作为分割网络，在脑部和腹部数据集上以补丁方式进行训练。我们最终的挑战提交包括 10 个在不同异常生成过程配置下训练的 U-Net，通过 5 个数据折叠进行训练。我们的方法在 MICCAI 举办的 2022 年医学离群分布挑战的样本级和像素级任务中取得了第一名。

提高医学图像的异常检测能力的更困难的合成异常

Harder synthetic anomalies to improve OoD detection in Medical Images

Imaging markers of cerebral small vessel disease provide valuable information
on brain health, but their manual assessment is time-consuming and hampered by
substantial intra- and interrater variability. Automated rating may benefit
biomedical research, as well as clinical assessment, but diagnostic reliability
of existing algorithms is unknown. Here, we present the results of the
\textit{VAscular Lesions DetectiOn and Segmentation} (\textit{Where is VALDO?})
challenge that was run as a satellite event at the international conference on
Medical Image Computing and Computer Aided Intervention (MICCAI) 2021. This
challenge aimed to promote the development of methods for automated detection
and segmentation of small and sparse imaging markers of cerebral small vessel
disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral
microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while
leveraging weak and noisy labels. Overall, 12 teams participated in the
challenge proposing solutions for one or more tasks (4 for Task 1 - EPVS, 9 for
Task 2 - Microbleeds and 6 for Task 3 - Lacunes). Multi-cohort data was used in
both training and evaluation. Results showed a large variability in performance
both across teams and across tasks, with promising results notably for Task 1 -
EPVS and Task 2 - Microbleeds and not practically useful results yet for Task 3
- Lacunes. It also highlighted the performance inconsistency across cases that
may deter use at an individual level, while still proving useful at a
population level.

该研究在国际医学影像处理和计算机辅助干预会议上举办的一个挑战赛中，提出了自动检测和分割脑部微小血管疾病成像标记的方法，尤其是对于 EPVS 和微出血的表现良好，但对于来自血管假定的干区缺损的实用性结果尚不明确。

VALDO 在哪里？MICCAI 2021 年血管病变检测和分割挑战

Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021

Accurate detection and localization for angiodysplasia lesions is an
important problem in early stage diagnostics of gastrointestinal bleeding and
anemia. Gold-standard for angiodysplasia detection and localization is
performed using wireless capsule endoscopy. This pill-like device is able to
produce thousand of high enough resolution images during one passage through
gastrointestinal tract. In this paper we present our winning solution for
MICCAI 2017 Endoscopic Vision SubChallenge: Angiodysplasia Detection and
Localization its further improvements over the state-of-the-art results using
several novel deep neural network architectures. It address the binary
segmentation problem, where every pixel in an image is labeled as an
angiodysplasia lesions or background. Then, we analyze connected component of
each predicted mask. Based on the analysis we developed a classifier that
predict angiodysplasia lesions (binary variable) and a detector for their
localization (center of a component). In this setting, our approach outperforms
other methods in every task subcategory for angiodysplasia detection and
localization thereby providing state-of-the-art results for these problems. The
source code for our solution is made publicly available at
this https URL

本文提出了一种基于深度神经网络的血管畸形检测和定位方法，应用于胃肠道出血和贫血的早期诊断，其在 MICCAI 2017 内镜视觉子挑战赛中表现出色，且优于目前的最新结果，其源代码已公开发布。