Accurate segmentation of lesions is crucial for diagnosis and treatment of early esophageal cancer (EEC). However, neither traditional nor deep learning-based methods up to today can meet the clinical requirements, with the mean Dice score - the most important metric in medical image analysis - hardly exceeding 0.75. In this paper, we present a novel deep learning approach for segmenting EEC lesions. Our approach stands out for its uniqueness, as it relies solely on a single image coming from one patient, forming the so-called "You-Only-Have-One" (YOHO) framework. On one hand, this "one-image-one-network" learning ensures complete patient privacy as it does not use any images from other patients as the training data. On the other hand, it avoids nearly all generalization-related problems since each trained network is applied only to the input image itself. In particular, we can push the training to "over-fitting" as much as possible to increase the segmentation accuracy. Our technical details include an interaction with clinical physicians to utilize their expertise, a geometry-based rendering of a single lesion image to generate the training set (the \emph{biggest} novelty), and an edge-enhanced UNet. We have evaluated YOHO over an EEC data-set created by ourselves and achieved a mean Dice score of 0.888, which represents a significant advance toward clinical applications.

翻译：病灶的精确分割对于早期食管癌（EEC）的诊断与治疗至关重要。然而，迄今为止，无论是传统方法还是深度学习方法均未能满足临床需求，医学图像分析中最重要的指标——平均Dice分数——几乎难以超过0.75。本文提出一种分割早期食管癌病灶的新型深度学习方法。该方法独特之处在于仅依赖单个患者的单张图像，形成所谓的"你仅有一张图像"（YOHO）框架。一方面，这种"单图单网络"学习方式完全不使用其他患者的图像作为训练数据，确保了完全的患者隐私保护；另一方面，由于每个训练好的网络仅应用于输入图像本身，该方法几乎避免了所有与泛化相关的问题。特别地，我们可以尽可能推动训练至"过拟合"状态以提升分割精度。技术细节包括：与临床医生的交互以利用其专业知识、基于几何变换的单个病灶图像渲染以生成训练集（最大创新点），以及边缘增强型UNet。我们在自建的早期食管癌数据集上评估YOHO方法，取得了0.888的平均Dice分数，这标志着向临床应用迈出了重要一步。