标题： 基于Hessian的轻量级神经网络在最小训练数据集上的脑血管分割 显示英文标题

标题： Hessian-based lightweight neural network for brain vessel segmentation on a minimal training dataset

摘要： 在脑磁共振血管造影（MRA）中准确分割血管对于成功的手术程序（如动脉瘤修复或搭桥手术）至关重要。目前，注释主要通过手动分割或经典方法（如Frangi滤波器）进行，这些方法通常准确性不足。神经网络已成为医学图像分割的强大工具，但其发展依赖于标注良好的训练数据集。然而，缺乏公开可用的具有详细脑血管注释的MRA数据集。为解决这一差距，我们提出了一种基于Hessian矩阵的新型半监督学习轻量级神经网络，用于复杂结构（如管状结构）的3D分割，我们将其命名为HessNet。该解决方案是一个基于Hessian的神经网络，仅有6000个参数。HessNet可以在CPU上运行，并显著降低训练神经网络的资源需求。在最小训练数据集上的血管分割准确性达到了最先进的结果。它帮助我们创建了一个大型的半自动标注的脑MRA图像脑血管数据集，基于IXI数据集（标注了200张图像）。在应用HessNet后，由三位专家在三位神经血管外科医生的监督下进行注释。它提供了高精度的血管分割，并使专家只需关注最复杂的重点案例。该数据集可在https://git.scinalytics.com/terilat/VesselDatasetPartly获取。 显示英文摘要

摘要： Accurate segmentation of blood vessels in brain magnetic resonance angiography (MRA) is essential for successful surgical procedures, such as aneurysm repair or bypass surgery. Currently, annotation is primarily performed through manual segmentation or classical methods, such as the Frangi filter, which often lack sufficient accuracy. Neural networks have emerged as powerful tools for medical image segmentation, but their development depends on well-annotated training datasets. However, there is a notable lack of publicly available MRA datasets with detailed brain vessel annotations. To address this gap, we propose a novel semi-supervised learning lightweight neural network with Hessian matrices on board for 3D segmentation of complex structures such as tubular structures, which we named HessNet. The solution is a Hessian-based neural network with only 6000 parameters. HessNet can run on the CPU and significantly reduces the resource requirements for training neural networks. The accuracy of vessel segmentation on a minimal training dataset reaches state-of-the-art results. It helps us create a large, semi-manually annotated brain vessel dataset of brain MRA images based on the IXI dataset (annotated 200 images). Annotation was performed by three experts under the supervision of three neurovascular surgeons after applying HessNet. It provides high accuracy of vessel segmentation and allows experts to focus only on the most complex important cases. The dataset is available at https://git.scinalytics.com/terilat/VesselDatasetPartly.