A brain tumor segmentation method based on attention mechanism DOI Creative Commons
Juan Cao,

Jinjia Liu,

Jiaran Chen

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 30, 2025

The rise in brain tumor incidence due to the global population aging has intensified need for precise segmentation methods clinical settings. Current networks often fail capture comprehensive contextual information and fine edge details of tumors, which are crucial accurate diagnosis treatment. To address these challenges, we introduce BSAU-Net, a novel algorithm that employs attention mechanisms feature extraction modules enhance performance. Our approach aims assist clinicians making more diagnostic therapeutic decisions. BSAU-Net incorporates an module (EA) based on Sobel operator, enhancing model's sensitivity regions while preserving contours. Additionally, spatial (SPA) is introduced establish correlations, critical segmentation. class imbalance, can hinder performance, propose BADLoss, loss function tailored mitigate this issue. Experimental results BraTS2018 BraTS2021 datasets demonstrate effectiveness achieving average Dice coefficients 0.7506 0.7556, PPV 0.7863 0.7843, 0.8998 0.9017, HD95 2.1701 2.1543, respectively. These highlight BSAU-Net's potential significantly improve practice.

Language: Английский

DLSAC-Net: An automated enhanced segmentation and classification network for lung diseases detection using chest X-Ray images DOI
Prashant Bhardwaj, Amanpreet Kaur

Multimedia Tools and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: April 10, 2025

Language: Английский

Citations

0
A Comprehensive Review of U‐Net and Its Variants: Advances and Applications in Medical Image Segmentation DOI Creative Commons
Jiangtao Wang, Nur Intan Raihana Ruhaiyem,

Fu Panpan

et al.

IET Image Processing, Journal Year: 2025, Volume and Issue: 19(1)

Published: Jan. 1, 2025

ABSTRACT Medical images often exhibit low and blurred contrast between lesions surrounding tissues, with considerable variation in lesion edges shapes even within the same disease, leading to significant challenges segmentation. Therefore, precise segmentation of has become an essential prerequisite for patient condition assessment formulation treatment plans. Significant achievements have been made research related U‐Net model recent years. It improves performance is extensively applied semantic medical offer technical support consistent quantitative analysis methods. First, this paper classifies image datasets on basis their imaging modalities then examines its various improvement models from perspective structural modifications. The objectives, innovative designs, limitations each approach are discussed detail. Second, we summarise four central mechanisms variant algorithms: jump‐connection mechanism, residual‐connection 3D‐UNet, transformer mechanism. Finally, examine relationships among core enhancement commonly utilized propose potential avenues strategies future advancements. This provides a systematic summary reference researchers fields, look forward designing more efficient stable network based network.

Language: Английский

Citations

0
An improved u-net for image segmentation of iron roughnecks on offshore oil drilling platforms DOI
Qingfeng Zhang,

Hua Zhu,

Yan Lv

et al.

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 117689 - 117689

Published: April 1, 2025

Language: Английский

Citations

0
MDSTransUNet: Multi-Scale Deep Supervised Transformer U-Net for COVID-19 Lung and Infection Segmentation DOI

Yidan Yan,

Beibei Hou, Junding Sun

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
A brain tumor segmentation method based on attention mechanism DOI Creative Commons
Juan Cao,

Jinjia Liu,

Jiaran Chen

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 30, 2025

The rise in brain tumor incidence due to the global population aging has intensified need for precise segmentation methods clinical settings. Current networks often fail capture comprehensive contextual information and fine edge details of tumors, which are crucial accurate diagnosis treatment. To address these challenges, we introduce BSAU-Net, a novel algorithm that employs attention mechanisms feature extraction modules enhance performance. Our approach aims assist clinicians making more diagnostic therapeutic decisions. BSAU-Net incorporates an module (EA) based on Sobel operator, enhancing model's sensitivity regions while preserving contours. Additionally, spatial (SPA) is introduced establish correlations, critical segmentation. class imbalance, can hinder performance, propose BADLoss, loss function tailored mitigate this issue. Experimental results BraTS2018 BraTS2021 datasets demonstrate effectiveness achieving average Dice coefficients 0.7506 0.7556, PPV 0.7863 0.7843, 0.8998 0.9017, HD95 2.1701 2.1543, respectively. These highlight BSAU-Net's potential significantly improve practice.

Language: Английский

Citations

0