Abstract
Obtaining large-scale medical data, annotated or unannotated, is challenging due to stringent privacy regulations and data protection policies. In addition, annotating medical images requires that domain experts manually delineate anatomical structures, making the process both time-consuming and costly. As a result, semi-supervised methods have gained popularity for reducing annotation costs. However, the performance of semi-supervised methods is heavily dependent on the availability of unannotated data, and their effectiveness declines when such data are scarce or absent. To overcome this limitation, we propose a simple, yet effective and computationally efficient approach for medical image segmentation that leverages only existing annotations. We propose BoundarySeg , a multi-task framework that incorporates organ boundary prediction as an auxiliary task to full organ segmentation, leveraging consistency between the two task predictions to provide additional supervision. This strategy improves segmentation accuracy, especially in low data regimes, allowing our method to achieve performance comparable to or exceeding state-of-the-art semi supervised approaches all without relying on unannotated data or increasing computational demands. Code will be released upon acceptance.
Abstract (translated)
获取大规模的医疗数据，无论是标注过的还是未标注的，由于严格的隐私法规和数据保护政策而颇具挑战。此外，对医学图像进行注释需要领域专家手动勾画解剖结构，这使得过程既耗时又昂贵。因此，半监督方法因能降低注释成本而在医学影像分割中越来越受欢迎。然而，这些方法的效果很大程度上依赖于未标注数据的可用性，并且当此类数据稀缺或缺失时效果会显著下降。 为克服这一限制，我们提出了一种简单而有效的计算高效方法，用于仅基于现有标注进行医疗图像分割。我们提出了BoundarySeg，这是一个多任务框架，它将器官边界预测作为全器官分割的一个辅助任务，并利用两个任务之间的一致性来提供额外的监督。这种策略提高了分割准确性，特别是在低数据条件下更为明显。因此，我们的方法可以在不依赖未标注数据或增加计算需求的情况下实现与当前最佳半监督方法相当甚至更好的性能。 代码将在论文被接受后发布。
URL
https://arxiv.org/abs/2505.09829
