分割
人工智能
计算机科学
图像分割
监督学习
深度学习
注释
尺度空间分割
模式识别(心理学)
人工神经网络
计算机视觉
作者
Yuhang Lu,Kang Zheng,Weijian Li,Yirui Wang,Adam P. Harrison,Chi‐Hung Lin,Song Wang,Jing Xiao,Le Lü,Chang‐Fu Kuo,Shun Miao
出处
期刊:IEEE Transactions on Medical Imaging
[Institute of Electrical and Electronics Engineers]
日期:2020-12-09
卷期号:40 (10): 2672-2684
被引量:19
标识
DOI:10.1109/tmi.2020.3043375
摘要
Accurate segmentation of anatomical structures is vital for medical image analysis. The state-of-the-art accuracy is typically achieved by supervised learning methods, where gathering the requisite expert-labeled image annotations in a scalable manner remains a main obstacle. Therefore, annotation-efficient methods that permit to produce accurate anatomical structure segmentation are highly desirable. In this work, we present Contour Transformer Network (CTN), a one-shot anatomy segmentation method with a naturally built-in human-in-the-loop mechanism. We formulate anatomy segmentation as a contour evolution process and model the evolution behavior by graph convolutional networks (GCNs). Training the CTN model requires only one labeled image exemplar and leverages additional unlabeled data through newly introduced loss functions that measure the global shape and appearance consistency of contours. On segmentation tasks of four different anatomies, we demonstrate that our one-shot learning method significantly outperforms non-learning-based methods and performs competitively to the state-of-the-art fully supervised deep learning methods. With minimal human-in-the-loop editing feedback, the segmentation performance can be further improved to surpass the fully supervised methods.
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