Semantic medical image segmentation using deep learning has recently achieved high accuracy, making it appealing to clinical problems such as radiation therapy. However, the lack of high-quality semantically labelled data remains a challenge leading to model brittleness to small shifts to input data. Most works require extra data for semi-supervised learning and lack the interpretability of the boundaries of the training data distribution during training, which is essential for model deployment in clinical practice. We propose a fully supervised generative framework that can achieve generalisable segmentation with only limited labelled data by simultaneously constructing an explorable manifold during training. The proposed approach creates medical image style paired with a segmentation task driven discriminator incorporating end-to-end adversarial training. The discriminator is generalised to small domain shifts as much as permissible by the training data, and the generator automatically diversifies the training samples using a manifold of input features learnt during segmentation. All the while, the discriminator guides the manifold learning by supervising the semantic content and fine-grained features separately during the image diversification. After training, visualisation of the learnt manifold from the generator is available to interpret the model limits. Experiments on a fully semantic, publicly available pelvis dataset demonstrated that our method is more generalisable to shifts than other state-of-the-art methods while being more explainable using an explorable manifold.

翻译：基于深度学习的语义医学图像分割近期取得了高精度，使其在放射治疗等临床问题中具有吸引力。然而，高质量语义标注数据的缺乏仍是一个挑战，导致模型对输入数据的微小偏移存在脆弱性。现有大多数方法需要额外数据进行半监督学习，且在训练过程中缺乏对训练数据分布边界的可解释性，而这对于模型在临床实践中的部署至关重要。我们提出一种全监督生成框架，通过在训练过程中同步构建可探索流形，仅利用有限标注数据即可实现具有泛化能力的分割。该方法将医学图像风格与分割任务驱动的判别器结合，采用端到端对抗训练。判别器在训练数据允许的范围内对小域偏移进行泛化，生成器则利用分割过程中学习的输入特征流形自动实现训练样本多样化。同时，判别器在图像多样化过程中通过分别监督语义内容和细粒度特征来引导流形学习。训练完成后，可调用生成器所学流形的可视化结果来解读模型局限性。在全语义公开骨盆数据集上的实验表明，我们的方法相比其他先进方法对偏移具有更强的泛化能力，同时通过可探索流形实现了更高的可解释性。