LesionMix: a lesion-level data augmentation method for medical image segmentation
File(s)
Accepted version
Author(s)
Type
Conference Paper
Abstract
Data augmentation has become a de facto component of deep learning-based medical image segmentation methods. Most data augmentation techniques used in medical imaging focus on spatial and intensity transformations to improve the diversity of training images. They are often designed at the image level, augmenting the full image, and do not pay attention to specific abnormalities within the image. Here, we present LesionMix, a novel and simple lesion-aware data augmentation method. It performs augmentation at the lesion level, increasing the diversity of lesion shape, location, intensity and load distribution, and allowing both lesion populating and inpainting. Experiments on different modalities and different lesion datasets, including four brain MR lesion datasets and one liver CT lesion dataset, demonstrate that LesionMix achieves promising performance in lesion image segmentation, outperforming several recent Mix-based data augmentation methods. The code will be released at https://github.com/dogabasaran/lesionmix.
Editor(s)
Xue, Y
Chen, C
Chen, C
Zuo, L
Liu, Y
Date Issued
2024-04-27
Date Acceptance
2023-10-01
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2024, 14379, pp.73-83
ISBN
978-3-031-58170-0
ISSN
0302-9743
Publisher
Springer
Start Page
73
End Page
83
Journal / Book Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
14379
Copyright Statement
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG.
Source
3rd International Workshop on Data Augmentation, Labeling, and Imperfections (DALI)
Subjects
Computer Science
Computer Science, Artificial Intelligence
Computer Science, Theory & Methods
Data augmentation
Image synthesis
Lesion image segmentation
Lesion inpainting
Lesion populating
Life Sciences & Biomedicine
Radiology, Nuclear Medicine & Medical Imaging
Science & Technology
Technology
Publication Status
Published
Start Date
2023-10-12
Finish Date
2023-10-12
Coverage Spatial
Vancouver, Canada
Date Publish Online
2024-04-27