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@article{175353,
        author = {Aditya Mokashi and Ansh Choudhary and Chiraag L and Darshitha G and Dr. S. Srividhya},
        title = {Enhancing Medical Image Segmentation in Abdominal Multi-Organ Segmentation: A Survey},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {2444-2449},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175353},
        abstract = {Medical image segmentation plays a crucial role in diagnosing and treating various diseases by accurately delineating anatomical structures. This paper explores deep learning-based segmentation techniques, focusing on attention mechanisms to enhance the accuracy of abdominal multi-organ segmentation. Traditional segmentation approaches, including thresholding, clustering, and edge detection, have limitations in handling complex medical images due to variations in intensity, shape, and noise. Recent advancements in deep learning, particularly the nnU-Net framework, have demonstrated improved performance in automated segmentation tasks. This study investigates the impact of attention mechanisms such as self-attention and transformer-based models in refining feature extraction and spatial awareness for better segmentation outcomes. A comparative evaluation of different attention-based architectures is conducted, assessing their performance using metrics such as Dice Similarity Coefficient (DSC), Intersection over Union (IoU), and Precision- Recall metrics. The results indicate that integrating attention mechanisms significantly improves segmentation accuracy, making it a viable approach for real-world medical applications. This survey aims to provide insights into the evolving landscape of medical image segmentation and highlights the potential of deep learning in advancing healthcare diagnostics.},
        keywords = {Deep Learning, Medical Image Segmentation, Attention Mechanism, nnU-Net, Multi-Organ Segmentation.},
        month = {April},
        }