Details

Autor(en) / Beteiligte

• Raj Singh, Kamal
• Sharma, Ambalika
• Kumar Singh, Girish

Titel

Attention-guided residual W-Net for supervised cardiac magnetic resonance imaging segmentation

Ist Teil von

• Biomedical signal processing and control, 2023-09, Vol.86, p.105177, Article 105177

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2023

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •A technique ARW-Net for segmenting cardiac magnetic resonance imaging is presented.•Residual connections, attention gates and redesigned deep supervision is implemented.•Extremely efficient in terms of segmentation accuracy and outperforms for DSC and HD.•State-of-the-art methods outperformed in segmentation results on four CMRI dataset.•Ablation study is conducted to verify the effectiveness of each module in ARW-Net. With latest developments in deep learning approaches, automated, accurate, fast, and generalized segmentation model for left atrium, left ventricle, right ventricle, and myocardium from MR images is becoming increasingly desirable. In deep learning-based approaches, model generalizability is an essential concern. The strength of an approach that has proven competent for one dataset, and then executed for other without fine-tuning, started to decline. In medical image segmentation, U-Net-based architecture with its skip-connection has attained the highest levels of success. This article, therefore presents a novel Attention-guided Residual W-Net (ARW-Net), a deep learning-based segmentation approach with residual links, attention gates at each feature dimensions, and upgraded deep supervision for cardiac MRI (CMRI) segmentation. As the basic building block, the W-Net, a unique methodology for medical image segmentation (centred on U-Net layout), is used. In a W-Net-based structure, ARW-Net suggests residual connections in encoder and attention gates at each feature dimension in decoder. Combination of Dice and Cross-entropy losses is used for model training. Four CMRI datasets, (i) ACDC 2017, (ii) 2018 ASC, (iii) M&Ms 2020, and (iv) LAScarQS 2022, which are publicly available, have been utilized to examine effectiveness of ARW-Net. It accomplished improved segmentation results and was among the top two for several metrics. Numerous comparisons have demonstrated that ARW-Net is profoundly promising, with several segmentation findings delivering innovative state-of-the-art outcomes on four datasets. The results of experiments demonstrate that ARW-Net is able to effectively segment cardiac MR images, as demonstrated by comparisons with existing algorithms.