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Cardiac magnetic resonance imaging (CMRI) segmentation transforms cardiac MR images into semantic regions to define the left ventricle cavity, right ventricle cavity, and myocardium. CMRI segmentation provides ventricles' volume, mass, and ejection fraction, playing a significant role in cardiac disease diagnosis. This paper aims to propose novel deep supervision in U-Net-based architecture to enhance the segmentation performance. It presents a deeply supervised W-Net, which creates another path in parallel with the decoder path in U-Net-based architecture. The output of every upsampling layer in the decoder path is combined with pixel-wise addition for feature reuse, and loss is computed at each feature dimension on the deep supervision layer, which enables gradients to be implanted at a greater depth into the network and enhances the training of all layers in the network. Proposed W-Net applied on single scanner-based ACDC dataset and Multi-Centre, Multi-Vendor & Multi-Disease dataset, making it more robust in model generalization. W-Net significantly outperforms numerous state-of-the-art methods on the two publicly available CMRI datasets, according to experiments conducted. W-Net obtained better segmentation results ranked in the top three for many metrics. It is evident that the proposed W-Net has considerable potential in CMRI segmentation, cardiac assessment, and disease diagnosis. [ABSTRACT FROM AUTHOR]

• 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. [ABSTRACT FROM AUTHOR]