Deep learning has become a valuable tool for the automation of certain medical image segmentation tasks, significantly relieving the workload of medical specialists. Some of these tasks require segmentation to be performed on a subset of the input dimensions, the most common case being 3D-to-2D. However, the performance of existing methods is strongly conditioned by the amount of labeled data available, as there is currently no data efficient method, e.g. transfer learning, that has been validated on these tasks. In this work, we propose a novel convolutional neural network (CNN) and self-supervised learning (SSL) method for label-efficient 3D-to-2D segmentation. The CNN is composed of a 3D encoder and a 2D decoder connected by novel 3D-to-2D blocks. The SSL method consists of reconstructing image pairs of modalities with different dimensionality. The approach has been validated in two tasks with clinical relevance: the en-face segmentation of geographic atrophy and reticular pseudodrusen in optical coherence tomography. Results on different datasets demonstrate that the proposed CNN significantly improves the state of the art in scenarios with limited labeled data by up to 8% in Dice score. Moreover, the proposed SSL method allows further improvement of this performance by up to 23%, and we show that the SSL is beneficial regardless of the network architecture.

翻译：深度学习已成为自动化特定医学图像分割任务的重要工具，显著减轻了医学专业人员的工作负担。部分任务需要对输入维度的子集进行分割，最常见的情况是三维到二维分割。然而，现有方法的性能严重受限于标注数据量，因为目前尚无经过此类任务验证的数据高效方法（例如迁移学习）。本文提出了一种新颖的卷积神经网络（CNN）与自监督学习（SSL）方法，用于标签高效的三维到二维分割。该CNN由通过创新的三维到二维模块连接的3D编码器和2D解码器组成。所提出的SSL方法包括重建不同维度的模态图像对。该方法已在两项具有临床相关性的任务中得到验证：光学相干断层扫描中地理萎缩和网状假性玻璃膜疣的正面分割。多个数据集的结果表明，在标注数据有限的情况下，所提出的CNN将Dice评分相比现有最优方法最高提升了8%。此外，所提出的SSL方法可将性能进一步最高提升23%，并且我们证明了无论网络架构如何，SSL均能带来性能提升。