Author:
Jiang Dalei,Wang Yin,Zhou Feng,Ma Hongtao,Zhang Wenting,Fang Weijia,Zhao Peng,Tong Zhou
Abstract
Abstract
Background
Image segmentation is a difficult and classic problem. It has a wide range of applications, one of which is skin lesion segmentation. Numerous researchers have made great efforts to tackle the problem, yet there is still no universal method in various application domains.
Results
We propose a novel approach that combines a deep convolutional neural network with a grabcut-like user interaction to tackle the interactive skin lesion segmentation problem. Slightly deviating from grabcut user interaction, our method uses boxes and clicks. In addition, contrary to existing interactive segmentation algorithms that combine the initial segmentation task with the following refinement task, we explicitly separate these tasks by designing individual sub-networks. One network is SBox-Net, and the other is Click-Net. SBox-Net is a full-fledged segmentation network that is built upon a pre-trained, state-of-the-art segmentation model, while Click-Net is a simple yet powerful network that combines feature maps extracted from SBox-Net and user clicks to residually refine the mistakes made by SBox-Net. Extensive experiments on two public datasets, PH2 and ISIC, confirm the effectiveness of our approach.
Conclusions
We present an interactive two-stage pipeline method for skin lesion segmentation, which was demonstrated to be effective in comprehensive experiments.
Funder
National Natural Science Foundation of China
Natural Science Foundation of Zhejiang Province
National Major Scientific and Technological Special Project for Significant New Drugs Development during the Thirteenth Five-year Plan Period
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Health Informatics,Computer Science Applications,Information Systems
Cited by
3 articles.
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