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
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.