Medical image segmentation based on adaptive threshold prediction and feature fusion

Wei Liu; Jiaqing Mo

doi:10.1117/12.3026449

27 March 2024 Medical image segmentation based on adaptive threshold prediction and feature fusion

Wei Liu, Jiaqing Mo

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131050K (2024) https://doi.org/10.1117/12.3026449
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

In the realm of computer-aided diagnosis, image segmentation techniques have garnered significant attention over the years. However, medical image data present distinctive characteristics, such as confidentiality, class imbalance, and challenges in acquiring accurate labels. Of certain rare medical conditions, it is arduous to obtain an adequate number of data samples within a limited scope for training models. Moreover, complex medical conditions may lead to diagnostic omissions concerning the locations of minor lesions. Consequently, our primary objective in this article is to address the challenge of enhancing the segmentation accuracy of lesion locations in complex medical conditions when working with limited data. Additionally, we believe that threshold selection and feature choice are particularly crucial for medical image segmentation tasks. However, existing medical image segmentation methods often prioritizes model complexity while overlooking the impact of threshold variations on segmentation performance. In this paper, we propose a multi-feature fusion network with adaptive threshold prediction and introduce a novel paradigm of semi-supervised pseudo-label generation to facilitate supervised learning. The multi-feature fusion module ensures the comprehensive utilization of dimension-sensitive features, while the adaptive threshold predictor enables the model to generate distinct segmentation thresholds based on varying inputs. We employed publicly available medical image datasets, namely Kvasir-SEG, Rim-one r2, and ISIC 2018, to train and validate our model and approach. In comparison to other supervised and semi-supervised methods, the model and the approach we proposed demonstrate superior segmentation performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wei Liu and Jiaqing Mo "Medical image segmentation based on adaptive threshold prediction and feature fusion", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131050K (27 March 2024); https://doi.org/10.1117/12.3026449

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image segmentation

Medical imaging

Feature fusion

Data modeling

Performance modeling

Image fusion

Image processing

Show All Keywords

Keywords/Phrases

Search In:

Publication Years