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：本文针对病理肺部分割问题展开研究，这是医学图像分析领域的重要挑战，在周边区域不透明（严重纤维化与实变）病例中尤为突出，其原因在于肺组织与周围区域的纹理相似性。为克服这些挑战，本文重点利用CycleGAN进行非配对图像到图像转换，以提供能够生成与现有真实标注匹配的伪病理图像的增强方法。尽管已有研究采用CycleGAN，但往往忽略形状变形这一对医学图像分割精度至关重要的挑战。本研究提出创新策略，引入额外的损失函数：特别设计了基于肺部周边区域的L1损失，该区域形状在从健康域到病理域的转换过程中被约束保持不变。肺部周边区域通过健康域中可用的真实肺部掩模推导得出。此外，通过基于肋骨/椎骨位置的裁剪等预处理步骤优化CycleGAN的输入，确保网络聚焦于肺部区域。这对避免无关偏差（如缩放效应偏差）至关重要，此类偏差可能分散主要任务的注意力。该方法通过采用U-Net模型增强半监督肺部分割过程，该模型结合由CycleGAN生成的合成病理组织进行实时数据增强训练。初步研究结果展示了显著的定性与定量改进，为病理肺部分割领域设立了新基准。代码公开于https://github.com/noureddinekhiati/Semi-supervised-lung-segmentation