Fourier Semi-Supervised Learning Method for Medical Image Segmentation

被引：0

作者：

Wang, Pengju ^{[1
]}

Zhang, Xiao ^{[1
]}

Ji, Zhenyan ^{[1
]}

Yu, Jin ^{[2
]}

Song, Yuezeng ^{[1
]}

机构：

[1] School of Software Engineering, Beijing Jiaotong University, Beijing

[2] Department of Neurology, First Medieal Center, PLA General Hospital, Beijing

来源：

Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications | 2024年 / 47卷 / 03期

关键词：

consistency regularization constraint; Fourier transformation; medical image segmentation; semi-supervised learning;

D O I：

10.13190/j.jbupt.2023-102

中图分类号：

学科分类号：

摘要：

The scarcity of labeled data is a challenging problem that affects the segmentation accuracy of medical images. Aiming to solve this problem, a new semi-supervised learning method based on Fourier transform and consistent constraintis proposed. In the case of a small amount of annotated data, the output of unannotated data via Fourier transform interpolation and model segmentation is spatially consistent with the output of reverse operation, and the consistency regularization constraint for unannotated data is constructed to improve the model performance of fully supervised learning. The experimental results, based on the openly available datasets ofthe automatic cardiac diagnosis challenge, synapse and computed tomography lymph node, demonstratethat the proposed algorithm is superior to baseline methods and can be integrated with existing state-of-the-art semi-supervised medical image segmentation methods to improve their segmentation performances. © 2024 Beijing University of Posts and Telecommunications. All rights reserved.

引用

页码：69 / 74and89

页数：7420

共 23 条

[1]

FU J F., Research and application of medical image segmentation algorithm based on semi-supervision [D], (2022)

[2]

FISCHER P, BROX T., U-Net: conventional networks for biomedical image segmentation [C], Lecture Notes in Computer Science, pp. 234-241, (2015)

[3]

YAO C, HU M, LI Q, Et al., Transclaw U-Wet: claw U-Net with transformers for medical image segmentation [C], 2022 5 th International Conference on Information Communication and Signal Processing, pp. 280-284, (2022)

[4]

HAN K, LIU L, SONG Y, Et al., An effective semi-supervised approach for liver CT image segmentation, IEEE Journal of Biomedical and Health Informatics, 26, 8, pp. 3999-4007, (2022)

[5]

VERMA V, LAMB A, KANNALA J, Et al., Interpolation consistency training for semi-supervised learning [J], Neural Networks

[6]

the Official Journal of the International Neural Network Society, 145, pp. 90-106, (2019)

[7]

GAO S, XU Q Z., Application of transfer learning in medical imaging, Computer Engineering and Applications, 57, 24, pp. 39-50, (2021)

[8]

ADAMS R, BISCHOF L., Seeded region growing, IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 641-647, (1994)

[9]

LEI Y, ZHANG L J, TANG P, Et al., Cell image segmentation algorithm based on color model minimum, Journal of Beijing University of Posts and Telecommunications, 45, 4, pp. 123-128, (2022)

[10]

DHANACHANDRA N, MANGLEM K, CHANU Y J., Image segmentation using &means clustering algorithm and subtractive clustering algorithm, Procedia Computer Science, 54, pp. 764-771, (2015)

← 1 2 3 →