Selective information passing for MR/CT image segmentation
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S.I. : DEEP SOCIAL COMPUTING
Selective information passing for MR/CT image segmentation Qikui Zhu1 • Liang Li2 • Jiangnan Hao3 • Yunfei Zha4 • Yan Zhang5 • Yanxiang Cheng6 Fei Liao7 • Pingxiang Li8
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Received: 25 May 2020 / Accepted: 29 September 2020 Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Automated medical image segmentation plays an important role in many clinical applications, which however is a very challenging task, due to complex background texture, lack of clear boundary and significant shape and texture variation between images. Many researchers proposed an encoder–decoder architecture with skip connections to combine low-level feature maps from the encoder path with high-level feature maps from the decoder path for automatically segmenting medical images. The skip connections have been shown to be effective in recovering fine-grained details of the target objects and may facilitate the gradient back-propagation. However, not all the feature maps transmitted by those connections contribute positively to the network performance. In this paper, to adaptively select useful information to pass through those skip connections, we propose a novel 3D network with self-supervised function, named selective information passing network. We evaluate our proposed model on the MICCAI Prostate MR Image Segmentation 2012 Grant Challenge dataset, TCIA Pancreas CT-82 and MICCAI 2017 Liver Tumor Segmentation Challenge dataset. The experimental results across these datasets show that our model achieved improved segmentation results and outperformed other state-ofthe-art methods. The source code of this work is available at https://github.com/ahukui/SIPNet. Keywords Medical image segmentation Convolutional neural network Attention-focused module
Qikui Zhu, Liang Li, Yunfei Zha and Pingxiang Li: co-first authors. & Yan Zhang [email protected]
1
School of Computer Science, Wuhan University, Wuhan, China
& Yanxiang Cheng [email protected]
2
Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
& Fei Liao [email protected]
3
Xi’an Aeronautical University, Xi’an, China
4
Qikui Zhu [email protected]
Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
5
Liang Li [email protected]
Department of Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, China
6
Jiangnan Hao [email protected]
Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, China
7
Yunfei Zha [email protected]
Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
8
Renmin Hospital of Wuhan University, Wuhan, China
Pingxiang Li [email protected]
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Neural Computing and Applications
1 Introduction Medical image segmentation is an essential part of medical image analysis. Accurate segmentation of medical image provides very useful information for computer-aided diagnosis and treatment of cancers as well as other diseases [1]. For
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