Rougé Pierre, Conze Pierre-Henri, Passat Nicolas, Merveille Odyssée
Université de Reims Champagne Ardenne, CRESTIC, Reims, France; Univ Lyon, INSA-Lyon, Universite Claude Bernard Lyon 1, CREATIS, Lyon, France.
IMT Atlantique, LaTIM UMR 1101, Inserm, Brest, France.
Comput Med Imaging Graph. 2025 Jan;119:102474. doi: 10.1016/j.compmedimag.2024.102474. Epub 2024 Dec 11.
Segmentation in medical imaging is an essential and often preliminary task in the image processing chain, driving numerous efforts towards the design of robust segmentation algorithms. Supervised learning methods achieve excellent performances when fed with a sufficient amount of labeled data. However, such labels are typically highly time-consuming, error-prone and expensive to produce. Alternatively, semi-supervised learning approaches leverage both labeled and unlabeled data, and are very useful when only a small fraction of the dataset is labeled. They are particularly useful for cerebrovascular segmentation, given that labeling a single volume requires several hours for an expert. In addition to the challenge posed by insufficient annotations, there are concerns regarding annotation consistency. The task of annotating the cerebrovascular tree is inherently ambiguous. Due to the discrete nature of images, the borders and extremities of vessels are often unclear. Consequently, annotations heavily rely on the expert subjectivity and on the underlying clinical objective. These discrepancies significantly increase the complexity of the segmentation task for the model and consequently impair the results. Consequently, it becomes imperative to provide clinicians with precise guidelines to improve the annotation process and construct more uniform datasets. In this article, we investigate the data dependency of deep learning methods within the context of imperfect data and semi-supervised learning, for cerebrovascular segmentation. Specifically, this study compares various state-of-the-art semi-supervised methods based on unsupervised regularization and evaluates their performance in diverse quantity and quality data scenarios. Based on these experiments, we provide guidelines for the annotation and training of cerebrovascular segmentation models.
医学成像中的分割是图像处理链中的一项基本且通常是初步的任务，推动了众多旨在设计强大分割算法的努力。当使用足够数量的标注数据时，监督学习方法能取得优异的性能。然而，这样的标注通常非常耗时、容易出错且成本高昂。相比之下，半监督学习方法利用标注数据和未标注数据，并且在只有一小部分数据集被标注时非常有用。它们对于脑血管分割特别有用，因为标注单个容积对于专家来说需要几个小时。除了标注不足带来的挑战外，还存在标注一致性的问题。标注脑血管树的任务本质上是模糊的。由于图像的离散性质，血管的边界和末端往往不清晰。因此，标注严重依赖于专家的主观性和潜在的临床目标。这些差异显著增加了模型分割任务的复杂性，从而影响了结果。因此，为临床医生提供精确的指导方针以改进标注过程并构建更统一的数据集变得势在必行。在本文中，我们在不完美数据和半监督学习的背景下，研究深度学习方法在脑血管分割中的数据依赖性。具体而言，本研究比较了基于无监督正则化的各种先进半监督方法，并评估它们在不同数量和质量数据场景下的性能。基于这些实验，我们为脑血管分割模型的标注和训练提供指导方针。
