ArticleScientific reports2021
A deep learning method for automatic segmentation of the bony orbit in MRI and CT images.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 54 citations in OpenAlex.
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- Automatic detection of optic canal fractures and recognition and segmentation of anatomical structures in the orbital apex based on artificial intelligence.Frontiers in cell and developmental biology · 2025Article
- Insights into Orbital Symmetry: A Comprehensive Retrospective Study of 372 Computed Tomography Scans.Journal of clinical medicine · 2024Article
- Nonspecific Orbital Inflammation (NSOI): Unraveling the Molecular Pathogenesis, Diagnostic Modalities, and Therapeutic Interventions.International journal of molecular sciences · 2024Review
- Investigation of autosegmentation techniques on T2-weighted MRI for off-line dose reconstruction in MR-linac workflow for head and neck cancers.Medical physics · 2024Article
- Automatic segmentation of inconstant fractured fragments for tibia/fibula from CT images using deep learning.Scientific reports · 2023Article
- Automatic segmentation of orbital wall from CT images via a thin wall region supervision-based multi-scale feature search network.International journal of computer assisted radiology and surgery · 2023Article
- Neural network application for assessing thyroid-associated orbitopathy activity using orbital computed tomography.Scientific reports · 2023Article
- Automated Segmentation of Levator Ani Muscle from 3D Endovaginal Ultrasound Images.Bioengineering (Basel, Switzerland) · 2023Article
- Deep-Learning-Based Segmentation of Extraocular Muscles from Magnetic Resonance Images.Bioengineering (Basel, Switzerland) · 2023Article
- Automatic Identification and Segmentation of Orbital Blowout Fractures Based on Artificial Intelligence.Translational vision science & technology · 2023Article
- Deep Learning-Based Medical Images Segmentation of Musculoskeletal Anatomical Structures: A Survey of Bottlenecks and Strategies.Bioengineering (Basel, Switzerland) · 2023Review
- Micro-CT and deep learning: Modern techniques and applications in insect morphology and neuroscience.Frontiers in insect science · 2023Review
- Article
- A high-throughput semi-automated bone segmentation workflow for murine hindpaw micro-CT datasets.Bone reports · 2022Article
- Deep Learning Analyses of Brain MRI to Identify Sustained Attention Deficit in Treated Obstructive Sleep Apnea: A Pilot Study.Sleep and vigilance · 2022Article
- Segmentation of orbital and periorbital lesions detected in orbital magnetic resonance imaging by deep learning method.Polish journal of radiology · 2022Article
- A Sequential Machine Learning-cum-Attention Mechanism for Effective Segmentation of Brain Tumor.Frontiers in oncology · 2022Article
- Orbital and eyelid diseases: The next breakthrough in artificial intelligence?Frontiers in cell and developmental biology · 2022Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper proposes a fully automatic method to segment the inner boundary of the bony orbit in two different image modalities: magnetic resonance imaging (MRI) and computed tomography (CT). The method, based on a deep learning architecture, uses two fully convolutional neural networks in series followed by a graph-search method to generate a boundary for the orbit. When compared to human performance for segmentation of both CT and MRI data, the proposed method achieves high Dice coefficients on both orbit and background, with scores of 0.813 and 0.975 in CT images and 0.930 and 0.995 in MRI images, showing a high degree of agreement with a manual segmentation by a human expert. Given the volumetric characteristics of these imaging modalities and the complexity and time-consuming nature of the segmentation of the orbital region in the human skull, it is often impractical to manually segment these images. Thus, the proposed method provides a valid clinical and research tool that performs similarly to the human observer.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.