Title: AssemblyNet: A large ensemble of CNNs for 3D Whole Brain MRI Segmentation Show Chinese title

Title: AssemblyNet：用于3D全脑MRI分割的大型CNN集合

Abstract: Whole brain segmentation using deep learning (DL) is a very challenging task since the number of anatomical labels is very high compared to the number of available training images. To address this problem, previous DL methods proposed to use a single convolution neural network (CNN) or few independent CNNs. In this paper, we present a novel ensemble method based on a large number of CNNs processing different overlapping brain areas. Inspired by parliamentary decision-making systems, we propose a framework called AssemblyNet, made of two "assemblies" of U-Nets. Such a parliamentary system is capable of dealing with complex decisions, unseen problem and reaching a consensus quickly. AssemblyNet introduces sharing of knowledge among neighboring U-Nets, an "amendment" procedure made by the second assembly at higher-resolution to refine the decision taken by the first one, and a final decision obtained by majority voting. During our validation, AssemblyNet showed competitive performance compared to state-of-the-art methods such as U-Net, Joint label fusion and SLANT. Moreover, we investigated the scan-rescan consistency and the robustness to disease effects of our method. These experiences demonstrated the reliability of AssemblyNet. Finally, we showed the interest of using semi-supervised learning to improve the performance of our method. Show Chinese abstract

Abstract: 使用深度学习（DL）进行全脑分割是一项极具挑战性的任务，因为解剖标签的数量相对于可用的训练图像数量来说非常大。 为了解决这个问题，以前的DL方法提出使用单个卷积神经网络（CNN）或少量独立的CNN。 在本文中，我们提出了一种基于大量处理不同重叠大脑区域的CNN的新型集成方法。 受议会决策系统的启发，我们提出了一种称为AssemblyNet的框架，由两个“委员会”组成的U-Net。 这种议会系统能够处理复杂决策、未见过的问题并快速达成共识。 AssemblyNet引入了相邻U-Net之间的知识共享，由第二个委员会在更高分辨率下进行的“修正”过程以优化第一个委员会的决策，并通过多数投票获得最终决策。 在我们的验证过程中，AssemblyNet的表现与最先进的方法如U-Net、联合标签融合和SLANT相当。 此外，我们研究了我们方法的扫描-重新扫描一致性以及对疾病效应的鲁棒性。 这些经验展示了AssemblyNet的可靠性。 最后，我们展示了使用半监督学习来提高我们方法性能的有益之处。