ArticleMultimedia tools and applications2022
AM-UNet: automated mini 3D end-to-end U-net based network for brain claustrum segmentation.
Article in Multimedia tools and applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 20 citations in OpenAlex.
- Multiscale characterization of the human claustrum from histology to MRI.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Deep learning techniques for using computed tomography imaging for hepatocellular carcinoma diagnosis, treatment and prognosis.World journal of gastroenterology · 2026Review
- A Contrast-Agnostic Method for Ultra-High Resolution Claustrum Segmentation.Human brain mapping · 2025Article
- A comprehensive and reliable protocol for manual segmentation of the human claustrum using high-resolution MRI.Brain structure & function · 2025Article
- Ipnet: informative patches learning for semi-supervised magnetic resonance image segmentation.Biomedical engineering letters · 2025Article
- gamUnet: designing global attention-based CNN architectures for enhanced oral cancer detection and segmentation.Frontiers in medicine · 2025Article
- Ratio maps of T1w/T2w MRI signal intensity do not improve deep-learning segmentation of pediatric brain tumors.PloS one · 2025Article
- Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look.Journal of cardiovascular development and disease · 2023Review
- Deep Learning-Based Segmentation of 3D Volumetric Image and Microstructural Analysis.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
Recent advances in deep learning (DL) have provided promising solutions to medical image segmentation. Among existing segmentation approaches, the U-Net-based methods have been used widely. However, very few U-Net-based studies have been conducted on automatic segmentation of the human brain claustrum (CL). The CL segmentation is challenging due to its thin, sheet-like structure, heterogeneity of its image modalities and formats, imperfect labels, and data imbalance. We propose an automatic optimized U-Net-based 3D segmentation model, called AM-UNet, designed as an end-to-end process of the pre and post-process techniques and a U-Net model for CL segmentation. It is a lightweight and scalable solution which has achieved the state-of-the-art accuracy for automatic CL segmentation on 3D magnetic resonance images (MRI). On the T1/T2 combined MRI CL dataset, AM-UNet has obtained excellent results, including Dice, Intersection over Union (IoU), and Intraclass Correlation Coefficient (ICC) scores of 82%, 70%, and 90%, respectively. We have conducted the comparative evaluation of AM-UNet with other pre-existing models for segmentation on the MRI CL dataset. As a result, medical experts confirmed the superiority of the proposed AM-UNet model for automatic CL segmentation. The source code and model of the AM-UNet project is publicly available on GitHub: https://github.com/AhmedAlbishri/AM-UNET.
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