This paper addresses the problem of pathological lung segmentation, a significant challenge in medical image analysis, particularly pronounced in cases of peripheral opacities (severe fibrosis and consolidation) because of the textural similarity between lung tissue and surrounding areas. To overcome these challenges, this paper emphasizes the use of CycleGAN for unpaired image-to-image translation, in order to provide an augmentation method able to generate fake pathological images matching an existing ground truth. Although previous studies have employed CycleGAN, they often neglect the challenge of shape deformation, which is crucial for accurate medical image segmentation. Our work introduces an innovative strategy that incorporates additional loss functions. Specifically, it proposes an L1 loss based on the lung surrounding which shape is constrained to remain unchanged at the transition from the healthy to pathological domains. The lung surrounding is derived based on ground truth lung masks available in the healthy domain. Furthermore, preprocessing steps, such as cropping based on ribs/vertebra locations, are applied to refine the input for the CycleGAN, ensuring that the network focus on the lung region. This is essential to avoid extraneous biases, such as the zoom effect bias, which can divert attention from the main task. The method is applied to enhance in semi-supervised manner the lung segmentation process by employing a U-Net model trained with on-the-fly data augmentation incorporating synthetic pathological tissues generated by the CycleGAN model. Preliminary results from this research demonstrate significant qualitative and quantitative improvements, setting a new benchmark in the field of pathological lung segmentation. Our code is available at https://github.com/noureddinekhiati/Semi-supervised-lung-segmentation

翻译：本文针对病理肺分割问题进行了研究，这是医学图像分析中的一个重大挑战，尤其在周边性浑浊（严重纤维化和实变）病例中尤为突出，原因是肺组织与周围区域的纹理相似性。为克服这些挑战，本文着重利用CycleGAN进行非配对图像到图像的转换，以提供一种能够生成与现有金标准匹配的虚拟病理图像的增强方法。尽管先前的研究已经采用了CycleGAN，但它们往往忽视了形状变形的挑战，而这对精确的医学图像分割至关重要。我们的工作引入了一种创新策略，该策略整合了额外的损失函数。具体来说，我们提出了一种基于肺周围的L1损失，其形状在从健康域到病理域的转换中保持不变。肺周围区域是基于健康域中可用的肺掩膜金标准导出的。此外，还应用了预处理步骤（例如基于肋骨/椎骨位置的裁剪）来优化CycleGAN的输入，确保网络聚焦于肺区域。这对于避免无关偏见（例如缩放效应偏见）至关重要，后者可能分散对主要任务的注意力。该方法通过采用配备CycleGAN模型生成的合成病理组织的实时数据增强技术训练的U-Net模型，以半监督方式增强肺分割过程。本研究的初步结果显示出了显著的定性和定量改进，为病理肺分割领域树立了新的基准。我们的代码可在https://github.com/noureddinekhiati/Semi-supervised-lung-segmentation 获取。