基于启发式迭代优化的形态学感知共识计算（MACCHIatO）
Morphologically-Aware Consensus Computation via Heuristics-based IterATive Optimization (MACCHIatO)
AI总结：
提出MACCHIatO方法，基于Fréchet均值和启发式优化计算共识分割，克服STAPLE对背景尺寸和先验的依赖，产生介于多数投票与STAPLE之间的二值掩膜及不同后验概率。
摘要：
从多个二值或概率掩膜中提取共识分割对于解决诸如评估评分者间变异性或融合多个神经网络输出等任务至关重要。获得这种共识分割最广泛使用的方法之一是STAPLE算法。在本文中，我们首先证明该算法的输出严重受到图像背景大小和先验选择的影响。然后，我们提出了一种新方法，基于精心选择的距离的Fréchet均值来构建二值或概率共识分割，使其完全独立于图像背景大小。我们提供了一种启发式方法来优化这一标准，使得体素的类别完全由其到不同掩膜的体素级距离、其所属的连通分量以及对其进行分割的评分者组决定。我们在多个数据集上广泛比较了我们的方法与STAPLE方法和朴素分割平均方法，结果表明，它产生的二值共识掩膜大小介于多数投票和STAPLE之间，并且产生的后验概率与掩膜平均和STAPLE方法不同。我们的代码可在 https://gitlab.inria.fr/dhamzaou/jaccardmap 获取。
英文摘要：
The extraction of consensus segmentations from several binary or probabilistic masks is important to solve various tasks such as the analysis of inter-rater variability or the fusion of several neural network outputs. One of the most widely used methods to obtain such a consensus segmentation is the STAPLE algorithm. In this paper, we first demonstrate that the output of that algorithm is heavily impacted by the background size of images and the choice of the prior. We then propose a new method to construct a binary or a probabilistic consensus segmentation based on the Fréchet means of carefully chosen distances which makes it totally independent of the image background size. We provide a heuristic approach to optimize this criterion such that a voxel's class is fully determined by its voxel-wise distance to the different masks, the connected component it belongs to and the group of raters who segmented it. We compared extensively our method on several datasets with the STAPLE method and the naive segmentation averaging method, showing that it leads to binary consensus masks of intermediate size between Majority Voting and STAPLE and to different posterior probabilities than Mask Averaging and STAPLE methods. Our code is available at https://gitlab.inria.fr/dhamzaou/jaccardmap .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
