Brain stroke has become a significant burden on global health and thus we
need remedies and prevention strategies to overcome this challenge. For this,
the immediate identification of stroke and risk stratification is the primary
task for clinicians. To aid expert clinicians, automated segmentation models
are crucial. In this work, we consider the publicly available dataset ATLAS
$v2.0$ to benchmark various end-to-end supervised U-Net style models.
Specifically, we have benchmarked models on both 2D and 3D brain images and
evaluated them using standard metrics. We have achieved the highest Dice score
of 0.583 on the 2D transformer-based model and 0.504 on the 3D residual U-Net
respectively. We have conducted the Wilcoxon test for 3D models to correlate
the relationship between predicted and actual stroke volume. For
reproducibility, the code and model weights are made publicly available:
this https URL

脑卒中已经成为全球健康负担，因此我们需要治疗方法和预防策略来克服这一挑战。在此工作中，我们考虑了公开可用的数据集 ATLAS v2.0，以评估各种端到端有监督的 U-Net 风格模型。我们在 2D 变换器模型上达到了最高的 Dice 得分 0.583，在 3D 残差 U-Net 模型上达到了 0.504。我们对 3D 模型进行了 Wilcoxon 测试，以确定预测和实际中风体积之间的关系。为了可复现性，代码和模型权重已公开提供。

BeSt-LeS：使用深度监督进行脑卒中损伤分割的基准评估

BeSt-LeS: Benchmarking Stroke Lesion Segmentation using Deep Supervision

Meningiomas are the most common primary intracranial tumor in adults and can
be associated with significant morbidity and mortality. Radiologists,
neurosurgeons, neuro-oncologists, and radiation oncologists rely on
multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal
treatment monitoring; yet automated, objective, and quantitative tools for
non-invasive assessment of meningiomas on mpMRI are lacking. The BraTS
meningioma 2023 challenge will provide a community standard and benchmark for
state-of-the-art automated intracranial meningioma segmentation models based on
the largest expert annotated multilabel meningioma mpMRI dataset to date.
Challenge competitors will develop automated segmentation models to predict
three distinct meningioma sub-regions on MRI including enhancing tumor,
non-enhancing tumor core, and surrounding nonenhancing T2/FLAIR hyperintensity.
Models will be evaluated on separate validation and held-out test datasets
using standardized metrics utilized across the BraTS 2023 series of challenges
including the Dice similarity coefficient and Hausdorff distance. The models
developed during the course of this challenge will aid in incorporation of
automated meningioma MRI segmentation into clinical practice, which will
ultimately improve care of patients with meningioma.

该论文介绍了 BraTS Meningioma 2023 挑战赛，该赛事提供了一个标准和基准，旨在利用自动化分割模型初步预测磁共振图像中的三种颅内脑膜瘤亚区域， 对模型的评估使用 Dice 系数和 Hausdorff 距离，并将改进脑膜瘤患者的治疗。