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arXiv · 2306.05912

Single-Image-Based Deep Learning for Segmentation of Early Esophageal Cancer Lesions

Haipeng Li·Dingrui Liu·Yu Zeng·Shuaicheng Liu·Tao Gan·Nini Rao·Jinlin Yang·Bing Zeng
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Abstract

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.

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Haipeng Li, Dingrui Liu, Yu Zeng, Shuaicheng Liu, Tao Gan, Nini Rao, Jinlin Yang, Bing Zeng. 2023-06-09. Single-Image-Based Deep Learning for Segmentation of Early Esophageal Cancer Lesions. https://arxiv.org/abs/2306.05912

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