Title: SemicNet: a semicircular network for the segmentation of the liver and its lesions

Authors: Zhihua Zheng; Victor S. Sheng; Lei Wang; Zhi Li; Xuefeng Xi; Zhiming Cui

Addresses: School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China ' Computer Science Department, Texas Tech University, Texas, DS-160, USA ' Touchair Tech, Suzhou, 215009, China ' Virtual Reality Key Laboratory of Intelligent Interaction and Application Technology of Suzhou, School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China ' Virtual Reality Key Laboratory of Intelligent Interaction and Application Technology of Suzhou, School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China ' Virtual Reality Key Laboratory of Intelligent Interaction and Application Technology of Suzhou, School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China

Abstract: The traditional neural network used for medical image segmentation was not clear on the network depth, the importance of different depths and the rationality of jump connection. In view of these problems, we propose a convenient and efficient liver and lesion segmentation system, which uses a double-layer codec semi-circular network to combine the deep and shallow semantic information through dense jump connection, which is easier to be processed by the optimiser; The transition zone between liver and lesion segmentation is designed so that the result of liver segmentation can be effectively transmitted to lesion segmentation; We believe that the selection of complementary loss function combination for in-depth supervision can effectively receive the anti-propagation gradient signal and obtain more regularisation effects. Finally, in terms of liver segmentation, in addition to the model with lower accuracy than multiple loss functions for joint decision-making, all other evaluation indexes, including lesions, exceeded the fusion results of multiple models.

Keywords: liver and lesion segmentation; medical image segmentation; deep learning; neural network; codec network; semantic segmentation.

DOI: 10.1504/IJSNET.2021.113838

International Journal of Sensor Networks, 2021 Vol.35 No.3, pp.161 - 169

Received: 21 Mar 2020
Accepted: 04 Jul 2020

Published online: 24 Mar 2021 *

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