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MADR-Net: multi-level attention dilated residual neural network for segmentation of medical images

التفاصيل البيبلوغرافية
العنوان: MADR-Net: multi-level attention dilated residual neural network for segmentation of medical images
المؤلفون: Keerthiveena Balraj, Manojkumar Ramteke, Shachi Mittal, Rohit Bhargava, Anurag S. Rathore
المصدر: Scientific Reports, Vol 14, Iss 1, Pp 1-20 (2024)
بيانات النشر: Nature Portfolio, 2024.
سنة النشر: 2024
المجموعة: LCC:Medicine
LCC:Science
مصطلحات موضوعية: Deep learning, Semantic segmentation, Convolutional neural networks, Class-spatial attention module, Atrous convolution, Medicine, Science
الوصف: Abstract Medical image segmentation has made a significant contribution towards delivering affordable healthcare by facilitating the automatic identification of anatomical structures and other regions of interest. Although convolution neural networks have become prominent in the field of medical image segmentation, they suffer from certain limitations. In this study, we present a reliable framework for producing performant outcomes for the segmentation of pathological structures of 2D medical images. Our framework consists of a novel deep learning architecture, called deep multi-level attention dilated residual neural network (MADR-Net), designed to improve the performance of medical image segmentation. MADR-Net uses a U-Net encoder/decoder backbone in combination with multi-level residual blocks and atrous pyramid scene parsing pooling. To improve the segmentation results, channel-spatial attention blocks were added in the skip connection to capture both the global and local features and superseded the bottleneck layer with an ASPP block. Furthermore, we introduce a hybrid loss function that has an excellent convergence property and enhances the performance of the medical image segmentation task. We extensively validated the proposed MADR-Net on four typical yet challenging medical image segmentation tasks: (1) Left ventricle, left atrium, and myocardial wall segmentation from Echocardiogram images in the CAMUS dataset, (2) Skin cancer segmentation from dermoscopy images in ISIC 2017 dataset, (3) Electron microscopy in FIB-SEM dataset, and (4) Fluid attenuated inversion recovery abnormality from MR images in LGG segmentation dataset. The proposed algorithm yielded significant results when compared to state-of-the-art architectures such as U-Net, Residual U-Net, and Attention U-Net. The proposed MADR-Net consistently outperformed the classical U-Net by 5.43%, 3.43%, and 3.92% relative improvement in terms of dice coefficient, respectively, for electron microscopy, dermoscopy, and MRI. The experimental results demonstrate superior performance on single and multi-class datasets and that the proposed MADR-Net can be utilized as a baseline for the assessment of cross-dataset and segmentation tasks.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2045-2322
Relation: https://doaj.org/toc/2045-2322
DOI: 10.1038/s41598-024-63538-2
URL الوصول: https://doaj.org/article/756d05bdcb7045e99c75b218bebc0c68
رقم الأكسشن: edsdoj.756d05bdcb7045e99c75b218bebc0c68
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20452322
DOI:10.1038/s41598-024-63538-2