ArticleBMC medical imaging2024
Multimodal Biomedical Image Segmentation using Multi-Dimensional U-Convolutional Neural Network.
Article in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Multimodality Artificial Intelligence for Involved-Site Radiation Therapy: Clinical Target Volume Delineation in High-Risk Pediatric Hodgkin Lymphoma.International journal of radiation oncology, biology, physics · 2026Article
- A Novel Dual-Modality Dual-View Hybrid Deep Learning-Machine Learning Framework for the Prediction of Carotid Plaque Vulnerability via Late Fusion.Diagnostics (Basel, Switzerland) · 2026Article
- An intelligent signal processing strategy for accurate quantification of composite signals in background fluorescence-quenching lateral flow immunoassay for folic acid.Mikrochimica acta · 2026Article
- Bridging modalities with AI: a review of AI advances in multimodal biomedical imaging.Communications engineering · 2026Review
- Radiomics early assessment of post chemotherapy cardiotoxicity in cancer patients using 2D echocardiography imaging an interpretable machine learning study.Scientific reports · 2025Article
- Advanced dynamic ensemble framework with explainability driven insights for precision brain tumor classification across datasets.Scientific reports · 2025Article
- Domain knowledge-infused pre-trained deep learning models for efficient white blood cell classification.Scientific reports · 2025Article
- Universal medical image segmentation via in-context cross-attention.Frontiers in artificial intelligence · 2025Article
- Applications, image analysis, and interpretation of computer vision in medical imaging.Frontiers in radiology · 2025Review
- Enhancing multiclass brain tumor diagnosis using SVM and innovative feature extraction techniques.Scientific reports · 2024Article
- AI-based automated breast cancer segmentation in ultrasound imaging based on Attention Gated Multi ResU-Net.PeerJ. Computer science · 2024Article
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6 authors.
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Abstract
Deep learning recently achieved advancement in the segmentation of medical images. In this regard, U-Net is the most predominant deep neural network, and its architecture is the most prevalent in the medical imaging society. Experiments conducted on difficult datasets directed us to the conclusion that the traditional U-Net framework appears to be deficient in certain respects, despite its overall excellence in segmenting multimodal medical images. Therefore, we propose several modifications to the existing cutting-edge U-Net model. The technical approach involves applying a Multi-Dimensional U-Convolutional Neural Network to achieve accurate segmentation of multimodal biomedical images, enhancing precision and comprehensiveness in identifying and analyzing structures across diverse imaging modalities. As a result of the enhancements, we propose a novel framework called Multi-Dimensional U-Convolutional Neural Network (MDU-CNN) as a potential successor to the U-Net framework. On a large set of multimodal medical images, we compared our proposed framework, MDU-CNN, to the classical U-Net. There have been small changes in the case of perfect images, and a huge improvement is obtained in the case of difficult images. We tested our model on five distinct datasets, each of which presented unique challenges, and found that it has obtained a better performance of 1.32%, 5.19%, 4.50%, 10.23% and 0.87%, respectively.
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