Yizhi Pan, Junyi Xin, Tianhua Yang, Siqi Li, Le-Minh Nguyen, Teeradaj Racharak, Kai Li, Guanqun Sun
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引用次数: 0
Abstract
Introduction: Accurate image segmentation is crucial in medical imaging for quantifying diseases, assessing prognosis, and evaluating treatment outcomes. However, existing methods often fall short in integrating global and local features in a meaningful way, failing to give sufficient attention to abnormal regions and boundary details in medical images. These limitations hinder the effectiveness of segmentation techniques in clinical settings. To address these issues, we propose a novel deep learning-based approach, MIPC-Net, designed for precise boundary segmentation in medical images.
Methods: Our approach, inspired by radiologists' working patterns, introduces two distinct modules: 1. Mutual Inclusion of Position and Channel Attention (MIPC) Module: To improve boundary segmentation precision, we present the MIPC module. This module enhances the focus on channel information while extracting position features and vice versa, effectively enhancing the segmentation of boundaries in medical images. 2. Skip-Residue Module: To optimize the restoration of medical images, we introduce Skip-Residue, a global residual connection. This module improves the integration of the encoder and decoder by filtering out irrelevant information and recovering the most crucial information lost during the feature extraction process.
Results: We evaluate the performance of MIPC-Net on three publicly accessible datasets: Synapse, ISIC2018-Task, and Segpc. The evaluation uses metrics such as the Dice coefficient (DSC) and Hausdorff Distance (HD). Our ablation study confirms that each module contributes to the overall improvement of segmentation quality. Notably, with the integration of both modules, our model outperforms state-of-the-art methods across all metrics. Specifically, MIPC-Net achieves a 2.23 mm reduction in Hausdorff Distance on the Synapse dataset, highlighting the model's enhanced capability for precise image boundary segmentation.
Conclusion: The introduction of the novel MIPC and Skip-Residue modules significantly improves feature extraction accuracy, leading to better boundary recognition in medical image segmentation tasks. Our approach demonstrates substantial improvements over existing methods, as evidenced by the results on benchmark datasets.
期刊介绍:
The translation of new discoveries in medicine to clinical routine has never been easy. During the second half of the last century, thanks to the progress in chemistry, biochemistry and pharmacology, we have seen the development and the application of a large number of drugs and devices aimed at the treatment of symptoms, blocking unwanted pathways and, in the case of infectious diseases, fighting the micro-organisms responsible. However, we are facing, today, a dramatic change in the therapeutic approach to pathologies and diseases. Indeed, the challenge of the present and the next decade is to fully restore the physiological status of the diseased organism and to completely regenerate tissue and organs when they are so seriously affected that treatments cannot be limited to the repression of symptoms or to the repair of damage. This is being made possible thanks to the major developments made in basic cell and molecular biology, including stem cell science, growth factor delivery, gene isolation and transfection, the advances in bioengineering and nanotechnology, including development of new biomaterials, biofabrication technologies and use of bioreactors, and the big improvements in diagnostic tools and imaging of cells, tissues and organs.
In today`s world, an enhancement of communication between multidisciplinary experts, together with the promotion of joint projects and close collaborations among scientists, engineers, industry people, regulatory agencies and physicians are absolute requirements for the success of any attempt to develop and clinically apply a new biological therapy or an innovative device involving the collective use of biomaterials, cells and/or bioactive molecules. “Frontiers in Bioengineering and Biotechnology” aspires to be a forum for all people involved in the process by bridging the gap too often existing between a discovery in the basic sciences and its clinical application.