Automatic segmentation of pericardial adipose tissue from cardiac MR images via semi‐supervised method with difference‐guided consistency DOI
Xinru Zhang,

Shoujun Zhou,

Bohan Li

et al.

Medical Physics, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 5, 2024

Abstract Background Accurate and automatic segmentation of pericardial adipose tissue (PEAT) in cardiac magnetic resonance (MR) images is essential for the diagnosis treatment cardiovascular diseases. Precise challenging due to high costs need specialized knowledge, as a large amount accurately annotated data required, demanding significant time medical resources. Purpose In order reduce burden annotation while maintaining accuracy tasks, this paper introduces semi‐supervised learning method solve limitations current PEAT methods. Methods paper, we propose difference‐guided collaborative mean teacher (DCMT) method, designed from DCMT consists two main components: framework with difference fusion strategy backbone network MCM‐UNet using Mamba‐CNN mixture (MCM) blocks. The differential effectively utilizes uncertain areas unlabeled data, encouraging model reach consensus predictions across these difficult‐to‐segment yet information‐rich areas. addition, considering sparse scattered distribution MR images, which makes it segment, our framework. This not only enhances processing ability global information, but also captures detailed local features image, greatly improves segmentation. Results Our experiments conducted on MRPEAT dataset show that outperforms existing state‐of‐the‐art methods terms accuracy. These findings underscore effectiveness approach handling specific challenges associated Conclusions significantly images. By utilizing enhancing feature capture MCM‐UNet, demonstrates superior performance offers promising solution image can alleviate extensive requirements typically necessary training accurate models imaging.

Language: Английский

Distribution-Aware Multi-Attention Tri-branch Networks with Feedforward Differential Features for semi-supervised medical image segmentation DOI
Pengcheng Shi, Shuchang Zhao, Lin Guo

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: 284, P. 127687 - 127687

Published: April 29, 2025

Language: Английский

Citations

0
Dual‐Region Consistency Learning With Contrastive Refinement for Semi‐Supervised Medical Image Segmentation DOI
Junmei Sun,

Meixi Wang,

Jianxiang Zhao

et al.

International Journal of Imaging Systems and Technology, Journal Year: 2025, Volume and Issue: 35(3)

Published: May 1, 2025

ABSTRACT Consistency regularization methods based on uncertainty estimation are a promising strategy for improving semi‐supervised medical image segmentation. However, existing consistency often neglect comprehensive feature extraction from both low and high regions. Additionally, the lack of class separability in segmentation limits learning more robust representations unlabeled images. To address these issues, this paper proposes novel framework named Dual‐Region Learning with Contrastive Refinement. The proposed Balanced (DRBCL) assigns different weights to regions predictions fully learn complete Furthermore, Hard Negative Samples (CLHNS) module incorporates idea contrastive learning. Positive hard negative sample pairs constructed by CLHNS further improve inter‐class contrast intra‐class In 10% labeled experiment, method achieves Dice coefficients 89.50% LA MR dataset 72.08% Pancreas CT dataset, which surpass benchmarks establishes new state‐of‐the‐art performance.

Language: Английский

Citations

0
Adaptive Semi-supervised Segmentation of Brain Vessels with Ambiguous Labels DOI
Fengming Lin, Yan Xia, Nishant Ravikumar

et al.

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 106 - 116

Published: Jan. 1, 2024

Language: Английский

Citations

3
Enhancing the vision-language foundation model with key semantic knowledge-emphasized report refinement DOI
Weijian Huang, Cheng Li, Hao Yang

et al.

Medical Image Analysis, Journal Year: 2024, Volume and Issue: 97, P. 103299 - 103299

Published: Aug. 13, 2024

Language: Английский

Citations

3
Automatic segmentation of pericardial adipose tissue from cardiac MR images via semi‐supervised method with difference‐guided consistency DOI
Xinru Zhang,

Shoujun Zhou,

Bohan Li

et al.

Medical Physics, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 5, 2024

Abstract Background Accurate and automatic segmentation of pericardial adipose tissue (PEAT) in cardiac magnetic resonance (MR) images is essential for the diagnosis treatment cardiovascular diseases. Precise challenging due to high costs need specialized knowledge, as a large amount accurately annotated data required, demanding significant time medical resources. Purpose In order reduce burden annotation while maintaining accuracy tasks, this paper introduces semi‐supervised learning method solve limitations current PEAT methods. Methods paper, we propose difference‐guided collaborative mean teacher (DCMT) method, designed from DCMT consists two main components: framework with difference fusion strategy backbone network MCM‐UNet using Mamba‐CNN mixture (MCM) blocks. The differential effectively utilizes uncertain areas unlabeled data, encouraging model reach consensus predictions across these difficult‐to‐segment yet information‐rich areas. addition, considering sparse scattered distribution MR images, which makes it segment, our framework. This not only enhances processing ability global information, but also captures detailed local features image, greatly improves segmentation. Results Our experiments conducted on MRPEAT dataset show that outperforms existing state‐of‐the‐art methods terms accuracy. These findings underscore effectiveness approach handling specific challenges associated Conclusions significantly images. By utilizing enhancing feature capture MCM‐UNet, demonstrates superior performance offers promising solution image can alleviate extensive requirements typically necessary training accurate models imaging.

Language: Английский

Citations

3